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This comprehensive narrative review investigates the function of sophisticated artificial intelligence algorithms, with particular emphasis on transformer-based architectures like GPT, in the analysis of human behavior within multifaceted domains, concentrating specifically on financial markets and the management of epidemics. The analysis delineates the chronological evolution from fundamental behavioral paradigms, such as prospect theory and the Health Belief Model, to modern AI-enhanced analytical methodologies that encapsulate intricate behavioral tendencies. Key trends in contemporary scholarly investigations underscore the increasing employment of cross-domain insights, facilitating the transference of models and algorithms from the financial sector to public health and other fields. Thorough assessments clarify the benefits in forecasting precision and flexibility, while also bringing attention to limitations related to data representation, model clarity, and moral considerations. Recognized weaknesses underscore the requirement for sustained validation regarding cross-disciplinary transfer, the combination of assorted data origins, and the implementation of transparent and fair AI frameworks. Practical examples show how AI-enhanced behavioral analysis can strengthen decision-making, refine risk management, craft intervention strategies, and influence policy creation, illustrating the value of such technologies in our community. The analysis ultimately determines that, although considerable advancements have been achieved, tackling the outstanding obstacles will augment both the empirical strength and pragmatic applicability of AI-facilitated behavioral modeling. These insights underscore the potential of blending diverse AI approaches to merge theoretical knowledge with hands-on application, thus laying a robust groundwork for future investigative efforts and community impact.