Global healthcare expenditures continue to rise, posing substantial economic challenges, particularly for low- and middle-income countries (LMICs), where resource constraints intensify the impact. Accurate forecasting, efficient resource allocation, and equitable policy development are essential to address these growing pressures. This study presents a hybrid analytical framework that integrates generative artificial intelligence (AI) with traditional econometric and machine learning models to analyze and predict trends of healthcare expenditure. Utilizing data from the World Bank and World Health Organization, we applied generative adversarial networks, hierarchical clustering, support vector machines, and autoregressive integrated moving average models to uncover spending patterns, simulate policy scenarios, and tackle disparities in health investment. Generative AI played a pivotal role by augmenting sparse and incomplete datasets, particularly from underrepresented LMICs, identifying anomalies, and generating realistic synthetic data to support robust forecasting. This enabled the development of more inclusive, equity-focused health resource planning tools. The results demonstrate improved forecasting accuracy and offer deeper insights into regional and income-based differences in expenditure trends. By combining traditional machine learning with cutting-edge generative models, this study advances a scalable, data-driven approach to support global health decision-making. Ultimately, generative AI is highlighted as a transformative enabler of equitable, informed strategies in the management of global healthcare expenditures.