The researcher investigates the contributions of AI and business analytics to IT project triumphs through their enhancement of project administration procedures along with resource distribution management and predictive risk avoidance analytics. The aftermath of the pandemic forced businesses in the USA to speed up their digital transformations by implementing artificial intelligence and business analytics to achieve IT project fulfillment. Technologies that integrate enable better decisions as well as exposure and operational effectiveness that result in systems with greater resilience and agility. The research design combines qualitative data collection methods with quantitative data collection methods through a mixed-methods approach. IT professionals with project managers based in the USA participate in research surveys to measure AI and business analytic effects on project accomplishment results. The researcher study leading tech companies through case studies to identify vital success elements and effective practices. Project performance correlations alongside digital transformation initiative patterns be identified through machine learning models and statistical analysis techniques. This research delivers important information about AI and business analytics effectiveness in supporting IT project success after the pandemic ends. The integration of new technologies enables organizations to create more effective project management practices and retain lower operational risks while advancing their innovation capabilities. AI capabilities and data analytics prove essential to support sustainable digital transformation that competes in the constantly evolving IT sector of the United States.

Modern-day AI infrastructure development requires more urgent need for reliable and efficient energy resources. Renewable energy obtains increasing attention, but coal-based energy generates substantial power in the worldwide energy consumption alongside emerging markets. The need for innovation becomes essential to optimize coal utilization because coal production contains efficiency problems alongside environmental challenges. The analysis draws data from multiple high-demand coal plant regions through their production logs with IoT sensors and their connected SCADA systems. Predictive models with machine learning algorithms, evaluate operational trends and breakdown patterns and environmental compliance performance. The implementation of BDA in AI-supported energy infrastructures is studied through case-based research that proves how better decisions, and reduced costs accompany balanced power distribution. Analysis of big data has proven to enhance coal-based energy operations its compatibility with AI-driven systems, which delivers better process efficiency and sustained energy production capabilities. The coal energy, artificial intelligence and big data analytics form a practical method to achieve smarter and more responsible energy operations in a data-centered environment. The study recommends political and energy sector investments in data infrastructure along with qualified personnel to bring out the complete advantages of these benefits.

This paper aims to review and analyze the connection between Business analytics and Management Information Systems in the Industry 4.0. The ability to facilitate decision-making and foster a sustainable intelligent environment in smart manufacturing companies. Industry 4.0 has brought the biggest change in the manufacturing industries today by providing more efficiency and sustainability. Launching Management Information Systems and business analytics, business intelligence is a key strategic tool that enables sustainable economic growth by improving an organization’s efficiency and cutting down on unnecessary spending. Examining the supply and demand of Management Information Systems and the effects of business analytics to support and accelerate sustainable economic growth. The research included case analysis of smart manufacturing firms and qualitative surveys with industry specialists. A real-time system environment was developed for manufacturing IoT innovative systems, with specific applications of predictive and prescriptive models. The sustainability and analysis capabilities of the Management Information Systems platforms are assessed. The study concludes that incorporating business analytics in smart manufacturing together with Management Information Systems offers a substantial enhancement to the economy’s sustainability. This is accomplished by improving resource use, reducing the output of waste, and using big data analysis for innovation. The study emphasizes how different Industry 4.0 technologies integrated into a proper Management Information Systems framework led to the development of a sustainable manufacturing environment. These aspects provide important information for any company that would like to integrate its performance goals with the general economic and environmental goals.

This paper seeks to delve into how shifting focus to AI-based predictive analytics. It has the potential to change the outlook in personalized medicine. Chronic diseases have become widespread and offer great challenges to the world’s healthcare system. It is calling for potential approaches to diagnosis and treatment. It is a revolutionary concept that predictive analytical model underpinned by AI that helps healthcare risk forecast and introduce timely intercessions. The patient data analyzed using AI-based models, which can thereby generate specific information used to make much more accurate diagnosis and treatment plans. This research is an exemplar of big data. It aims at using algorithms in machine learning to process EHRs, genotype and phenotype data and lifestyle data among individuals in the world. The data are used for the educational process of building the predictive models, which assists in the early diagnosis of chronic diseases. It includes diabetes, cardiovascular diseases, cancer and others. The methods are used to supervised learning ranging from decision tree algorithms and support vector machines to neural networks where required. The real-time data analysis is included in the study for the purpose of real-time monitoring and risk analysis for proper healthcare preventative methodologies. The insights derived show that AI-advanced analytics of the chronic disease patients’ data hold massive opportunities to redefine chronic illness treatment. AI for early detection of cases does not only lead to better care of patients but also helps lower health care expenditures since fewer intensive treatments are required. The proactive approach in connection with the individualization of treatment with the help of intelligent analytics is aimed at providing patient-tailored care.

The vulnerability of worldwide food supply bases, primarily on cereal crops, grows be-cause of drought conditions that result from climate change. The effective analysis of drought tolerance requires advanced research methods over traditional breeding practices because these methodologies do not fully describe molecular environmental factors with physiological components simultaneously. A complete understanding of drought stress mechanisms emerges from employing modern data analysis techniques on combined genomics and transcriptomics and proteomics and metabolomics and phenomics data-bases. The investigation presents multi-omics integration as a method to boost cli-mate-smart agriculture by revealing important drought-responsive genetic elements and metabolic routes and networks in cereal farming systems. Researchers implemented a complete multi-omics assessment technique to study drought reactions in crucial cereal crops, including rice and wheat, alongside maize. The research employed multiple sets of each tool, including high-throughput sequencing generation and mass spectrometry systems and imaging systems. Physiological characteristics with agricultural traits were studied to align molecular data with drought-tolerant specific genetic variants. The re-search identifies essential drought tolerance biomarkers, including genetic markers and protein and metabolite signatures which present breeding possibilities through genetic manipulation. Smokeless agriculture progress through this combined approach, which leads to sustainable agricultural production systems under climate change conditions.

This paper seeks to delve into how shifting focus to AI-based predictive analytics. It has the potential to change the outlook in personalized medicine. Chronic diseases have become widespread and offer great challenges to the world’s healthcare system. It is calling for potential approaches to diagnosis and treatment. It is a revolutionary concept that predictive analytical model underpinned by AI that helps healthcare risk forecast and introduce timely intercessions. The patient data analyzed using AI-based models, which can thereby generate specific information used to make much more accurate diagnosis and treatment plans. This research is an exemplar of big data. It aims at using algorithms in machine learning to process EHRs, genotype and phenotype data and lifestyle data among individuals in the world. The data are used for the educational process of building the predictive models, which assists in the early diagnosis of chronic diseases. It includes diabetes, cardiovascular diseases, cancer and others. The methods are used to supervised learning ranging from decision tree algorithms and support vector machines to neural networks where required. The real-time data analysis is included in the study for the purpose of real-time monitoring and risk analysis for proper healthcare preventative methodologies. The insights derived show that AI-advanced analytics of the chronic disease patients’ data hold massive opportunities to redefine chronic illness treatment. AI for early detection of cases does not only lead to better care of patients but also helps lower health care expenditures since fewer intensive treatments are required. The proactive approach in connection with the individualization of treatment with the help of intelligent analytics is aimed at providing patient-tailored care.