Artificial Intelligence and Machine Learning Across Domains: From Healthcare and Genomics to Supply Chains and Sustainable Development

Artificial Intelligence (AI) and Machine Learning (ML) have rapidly evolved into transformative technologies that are reshaping diverse sectors, ranging from healthcare and genomics to global supply chain management and sustainable development initiatives. In healthcare, AI-driven diagnostic models and ML-based predictive analytics are improving patient outcomes, personalizing treatment plans, and accelerating drug discovery. Similarly, genomics research increasingly relies on deep learning techniques for gene sequencing, variant detection, and precision medicine, unlocking new frontiers in human biology. Beyond life sciences, AI and ML algorithms enhance supply chain resilience by optimizing demand forecasting, inventory management, and logistics operations under uncertain market conditions. Furthermore, these technologies contribute significantly to sustainability efforts, including energy optimization, climate change mitigation, smart agriculture, and resource-efficient production. Despite their potential, challenges such as data privacy, algorithmic bias, and the need for transparent governance frameworks remain critical considerations. This paper explores the cross-domain applications of AI and ML, highlights their current and emerging contributions, and examines the implications for innovation, ethics, and policy in building sustainable and intelligent systems.