Predictive modeling technologies are revolutionizing the management of tropical diseases by providing precise forecasting, comprehensive risk assessment, and strategic intervention planning. This chapter examines the integration of advanced tools such as machine learning, artificial intelligence (AI), remote sensing, and Geographic Information Systems (GIS) to enhance disease surveillance, optimize resource allocation, and improve intervention strategies. Tropical diseases such as malaria, dengue fever, and cholera disproportionately impact vulnerable populations in resource-limited settings, exacerbating health disparities. Leveraging these technological innovations, health professionals can harness real-time data to predict outbreaks, deploy resources efficiently, and implement targeted interventions. The chapter explores how predictive modeling enhances early warning systems, guides vaccine distribution, and strengthens public health strategies. It also addresses challenges, including ethical concerns surrounding data privacy, computational limitations in resource-constrained environments, and the trade-off between model accuracy and interpretability for practical applications. Case studies were used to illustrate the successful application of predictive modeling technologies in improving disease surveillance and response, emphasizing their role in mitigating health inequities. Incorporating predictive modeling into public health systems, tropical disease management becomes more proactive, equitable, and sustainable. These technologies reduce the economic and social burden of diseases and contribute to achieving sustainable development goals by fostering health equity, enhancing healthcare resilience, and incorporating environmental factors into disease management. As predictive modeling technologies evolve, interdisciplinary collaboration among researchers, technologists, and policymakers will be essential to fully realize their potential in combating tropical diseases and advancing global health equity.