Artificial Intelligence (AI) is increasingly pivotal in addressing global challenges by integrating technological advancements into environmental conservation and societal development. This paper explores the dual nature of AI's role—its capacity to foster sustainability and societal improvement, alongside the challenges it introduces. The research highlights AI applications in environmental management, such as optimizing resource use, monitoring ecosystems, and enhancing renewable energy efficiency. In societal contexts, AI's transformative potential extends to healthcare, education, and urban planning, fostering equitable access to essential services. However, significant barriers, including data scarcity, limited technical infrastructure, regulatory gaps, and socio-economic inequalities, hinder widespread AI adoption. By analyzing secondary data from recent literature, this study identifies critical challenges and presents actionable policy recommendations to ensure inclusive, ethical, and impactful AI integration. The findings underscore the urgency of collaborative efforts among governments, industries, and academia to leverage AI for achieving the United Nations Sustainable Development Goals (SDGs) and addressing pressing global issues.

Early warning systems (EWS) are crucial for disaster risk reduction, providing timely and reliable information to communities and authorities for proactive mitigation. Traditional methods, such as weather stations, river gauges, and seismic networks, have limitations in spatial coverage, real-time data availability, and precursor signal detection. Recent technological advancements have enhanced EWS by integrating remote sensing data from satellites, airborne platforms, and ground-based sensors, enabling real-time monitoring of phenomena like wildfires, volcanic activity, and landslides. The Internet of Things (IoT) and crowdsourced data from social media, mobile apps, and citizen reports have further improved situational awareness and response times, complementing traditional systems. Increased computational power has enabled the development of sophisticated models, such as numerical weather prediction and seismic hazard models, which predict disaster impacts more accurately. Despite these advancements, challenges remain in data interoperability, resilient communication infrastructure, and delivering clear, actionable alerts to at-risk populations. Future EWS will likely become more data-driven and interconnected, leveraging artificial intelligence, big data analytics, and IoT. Collaboration among governments, academic institutions, and local communities is essential to building robust, inclusive EWS that save lives and reduce the economic impact of disasters.

Climate Information Services (CIS) serve as vital assets for Africa in navigating the intricate challenges of climate change. They offer prompt, reliable, and easily understandable climate information, enabling individuals, local communities, and organizations to make wise decisions that foster resilience and adaptation to the shifting climate. These services play a pivotal role in minimizing the detrimental impacts of climate change, thereby promoting sustainable growth throughout the continent. Utilizing an extensive research approach encompassing various techniques, our findings demonstrate that CIS positively impacts agricultural yield and economic gains, yet simultaneously augments climate change risk and vulnerability, while boosting resilience. The study underscores the escalating implementation and dissemination of CIS across various African territories. Despite the beneficial influence of CIS, the investigation exposed hurdles in capacity development and gender imbalances. The study's findings underscore the pivotal role that CIS plays in promoting sustainable development in Africa. To fully capitalize on these advantages, it is imperative to allocate strategic investments in infrastructure, capacity development, and information dissemination. For example, enhancing internet access in isolated regions, educating local professionals in climate science, and amplifying public awareness regarding the significance of CIS can substantially enhance their efficiency. Furthermore, nurturing collaborations at national and regional levels can help bridge existing disparities and guarantee that CIS reach those who require them most. Through addressing these challenges and implementing the recommended strategies, Africa can realize the full potential of climate information services. This will not only strengthen the continent's climate resilience but also promote economic growth and improve the livelihoods of millions of people. Ultimately, the successful integration of the CIS into Africa's development agenda will contribute to a more sustainable and climate-resilient future for the continent.

Sub-Saharan Africa (SSA) faces persistent food insecurity due to low agricultural productivity, limited access to modern technologies, and growing climate variability. This study explores the transformative potential of Artificial Intelligence (AI) to enhance food systems across SSA. The objective is to assess how AI applications—such as machine learning, remote sensing, and big data analytics—can address systemic inefficiencies in cereal crop production, with a focus on barley, millet, and sorghum. Using a systematic review approach aligned with PRISMA guidelines, literature from 2015–2025 was analyzed across multiple databases to identify empirical studies and models related to AI in SSA agriculture. Results reveal that AI can significantly improve crop monitoring, yield forecasting, and resource optimization. However, adoption barriers such as inadequate infrastructure, financial constraints, and the digital divide persist. The study concludes that while AI holds significant promise, its success in SSA depends on inclusive policies, capacity building, and localized data governance. It recommends interdisciplinary research, investment in rural digital infrastructure, and participatory innovation frameworks to empower smallholder farmers and ensure equitable AI deployment. This review provides a roadmap for integrating AI into SSA food systems to enhance resilience, productivity, and food security.

The healthcare sector is witnessing significant development in many aspects thanks to the effects of artificial intelligence or software, which has turned out to be the centre of attraction all over the world. This is evidence of a simple development in acquiring deep knowledge of the methods and areas in which they are used. Face detection, voice recognition, autonomous use, the defence industry, the security industry, and other fields may be displayed as examples that help complete tasks. This article surveys the impact of deep learning methods and practices in the medical device industry, and we also examine the distribution of multi-year data. It is divided into six categories: healthcare, big data and wearable technologies, biomedical code, image processing, diagnostics, and the Internet of Medical Things. As a result, the medical device industry has grown in recent years through deep learning techniques and the use of most research related to diagnosis and image processing.

Climate change poses significant challenges to global agriculture, necessitating innovative solutions to enhance sustainability and productivity. Artificial intelligence (AI) has emerged as a key enabler in climate-smart agricultural technologies (CSAT), offering data-driven approaches to optimize resource use, mitigate climate risks, and improve decision-making. This study aims to evaluate AI's integration into CSAT, focusing on its applications, benefits, and adoption challenges, particularly in climate-vulnerable regions. A bibliographic review employing machine learning (ML) and natural language processing (NLP) techniques was conducted to analyze over 40,000 scientific articles from global academic databases. Topic modeling and classification algorithms were applied to identify key trends, adoption barriers, and implementation pathways for AI-driven CSAT. The study also incorporated expert validation through the Delphi method to refine AI-generated insights and ensure their alignment with real-world agricultural challenges. Findings indicate that AI enhances decision-making in conservation agriculture, precision farming, water management, and market intelligence. AI-powered tools facilitate early pest detection, optimize irrigation schedules, and provide real-time climate advisory services, significantly improving agricultural resilience and food security. However, major barriers to AI adoption include high implementation costs, limited digital literacy, and inadequate infrastructure, particularly in low-income regions. Despite these challenges, AI-driven CSAT presents significant potential to transform agriculture, especially in climate-affected areas. Strategic investments in digital literacy, infrastructure development, and supportive policy frameworks are essential to facilitate AI adoption. Strengthening interdisciplinary collaboration among researchers, policymakers, and farmers will be crucial in advancing sustainable agricultural practices and ensuring long-term food security.