AI Across Multiple Domains: A Comprehensive Review

Abstract

Artificial Intelligence (AI) has gone from being a small branch of computation to becoming a ubiquitous and all-embracing influence in all aspects of human endeavour. This comprehensive The multidisciplinary review brings together information from more than 160 peer-reviewed publications from the previous 2020 to 2026, including healthcare and medical imaging, cybersecurity and intrusion detection, marketing, and others. and supply chain management, education, agriculture, consumer behaviour, finance and economics, In arts, humanities, law and the physical sciences. AI technologies are classified into eight main categories. It is compatible with families Deep Learning, Transformer Models, Explainable AI (XAI), Generative AI, Federated Learning. Know and be able to compare Quantum Machine Learning, Transfer Learning, and Optimisation Algorithms Deployment, performance and societal implications, both at the domain level and across domains. The deep findings are key points that are usually evident at a deeper level. learning dominates medical imaging with state-of-the-art segmentation accuracy; federated learning allows you to leverage the power of the group.federated learning lets you harness the power of the group. Leading the pack of reshaping technologies for cybersecurity is and hybrid CNN-LSTM architectures and for marketing is generative AI. Consumer engagement; and quantum-enhanced models are at the fore front of precision diagnostics  andencryption. Wemap critical gaps that encompass cross-domain data interoperability, ethical considerations and ethics, and the representation of data. Critically discuss governance, explainability and equitable deployment, and present a single research agenda for the next decade.