Business analytics has undergone significant transformation because of artificial intelligence and machine learning evolution, which now fulfills a critical function in economic expansion and organizational decision-making operations. Organization within the United States economy utilizes Management Information Systems to gain AI-powered insights, which enhance productivity and optimize resources and market prediction accuracy. The research evaluates how AI analytics drive economic growth because they enhance predictions while reducing possible hazards and generate strategic decisions through data-based approaches. The research uses both quantitative data methods together with qualitative case studies to investigate the subject. A combination of secondary market data, business reports, and economic statistics undergoes ML algorithm analysis, which reveals economic patterns and correlations because of their impact on performance. The research obtains practical information and usage barriers from both business managers and policymakers by conducting structured interviews regarding their implementation experiences with AI-based economic decision systems. The paper examines three significant AI methods, including predictive analytics with natural language processing and deep learning, which are applied to business intelligence. Voluntary business analytics, which run on artificial intelligence systems, boost decision-making by generating instant analytic information and simplifying complicated economic analysis tasks. Organizations that integrate AI and MIS systems to build data-based strategies boost operational performance while gaining competitive markets and ensuring durable economic expansion. Organizations should resolve the integration challenges together with data privacy concerns and ethical issues. The research findings demonstrate why organizations need to adopt AI-based analytics systems for developing business resilience and economic innovation in the United States.

This research demonstrates how MIS transforms energy independence through combined database integration of national energy boards with environmental regulatory agencies and smart grid technology systems. Numerous countries are now adopting Energy Dominance Strategies to boost home energy production decreasing import dependency preserving environmental sustainability during rising worldwide energy requirements. Management Information Systems function as a crucial intervention to link energy policies improving resource management and real-time decision processes. The study employs a mixed-method approach. Reports and dashboards provided by the U.S. Department of Energy International Energy Agency and Energy Information Administration between 2010 and 2024 supplied the quantitative data. The implementation of MIS resulted in a detailed assessment of key performance indicators which included energy output and operational costs combined with system downtime along with carbon emissions during before and after periods. Thirty executives along with IT professionals working in national oil and gas sectors and renewable energy industries and government departments provided qualitative insights through semi-structured interviews. The analysis demonstrates that MIS serves as more than a support tool by enabling the strategic needs of energy dominance through digital systems for sustainable resilient energy networks.

This research examines how MIS frameworks strengthen energy infrastructure resilience through consolidated use of predictive models alongside data analytics and crisis management resources. There are multi factors, such as escalating natural disasters and elevated cyber threats with aging infrastructure systems, constantly push. The U.S. energy system toward declining resilience levels. The strategic decision-making and performance enhancement now depends heavily on Management Information Systems. This study uses qualitative research methods and relies on secondary data from energy reports alongside energy grid failure analysis and MIS implementation studies. This analysis reviews various MIS systems, such as SCADA and ERP, to identify how they could improve monitoring operations and evaluation procedures and quick response functionality. MIS development based on specific system needs leads to greater energy system surveillance capabilities and better resource management with improved recovery protocols. The research demonstrates how energy infrastructure protection improves when intelligent MIS combines real-time analysis along with predictive artificial intelligence technologies towards reaching national security goals for U.S. energy systems.

Consumers now have more options and comparison shopping when making purchases thanks to the new retail industry's quick expansion. This has increased rivalry among supermarket chains and continuously compressed profit margins. This research uses big data analytics to compare several ML approaches for forecasting grocery sales. The purpose of the research is to assess how well different ML algorithms predict sales. This paper uses supermarket sales data as the database, uses Python to conduct data preprocessing. By analyzing this data, including identifying anomalies and trends through data mining techniques, develop predictive models using advanced machine learning algorithms including DT, XGBoost, GB, and RF are employed to forecast sales volumes with greater accuracy than traditional methods across MAE, and R2-score. In evaluating model performance, the Extra Trees (ET) model exhibits superior accuracy with an R² of 0.94 and MAE of 1.96, compared to the existing models. Addressing challenges such as data sparsity, variability, and adapting to dynamic market conditions will be crucial. Additionally, future research could investigate novel methods for combining diverse forecasting approaches and refining models to better handle the complexities of real-world sales data.

The research explores how business analytics supports sustainable management by aligning organizational objectives with sustainable development goals. It highlights the importance of environmental and social responsibilities for organizations. Business analytics provides valuable insights by turning collected data into actionable value. The study uses online survey data and case study analysis to analyze secondary data from industries like manufacturing, retailing, and energy. The research shows that business analytics helps organizations understand process weaknesses and improve operational sustainability by eliminating or minimizing waste. By bridging the gap between data and actionable value generation, business analytics helps organizations meet international sustainability standards and contribute to sustainable management.

The role of management information systems is to act as an enabler for incorporating predictive analytics as a support system in the supply chain. There is now an emphasis on supply chain management. Predictive analytics particularly on the supply chain vulnerability due to the various economic instabilities such as those that result from global calamities, natural disasters and fluctuations in the markets. The integration of two fields of predictive analytics and Management Information Systems. Their functions in managing risks and facilitating flexibility in SCM are discussed. The study uses the case study analysis as well as model simulation. Supply chain data from around the world are processed with complex tools of predictive analytics such as time series analysis, regression analysis, and machine learning. Management Information Systems platforms are developed with an interface that allows data to be fed in real-time and for real-time decision-making. The results indicate that when the Management Information Systems has embedded the predictive analytics. The supply chain vulnerability is significantly reduced and resources effectively be successfully marshalled. The organizations that have adopted this approach seem to have attained higher operational efficiency, less economic loss and the ability to rebound to disruptions. The relevance of using predictive analysis within Management Information Systems as a tactical approach to enhancing strong and flexible supply chain functionality is critical in coping with the complexity of the current global economic environment.