The combination of geographic information system (GIS), artificial intelligence (AI), and machine learning (ML) is transforming environmental health monitoring by providing improved tools for analysing, forecasting, and mitigating environmental issues. GIS is superior in spatial visualization and analysis, enabling better understanding of environmental trends and their impact on health. AI and ML supplement GIS with predictive power, decision-making automation, and accuracy. Recent developments have witnessed AI-based systems analysing real-time IoT sensor data and satellite imagery to predict early warning of pollution peaks and health risks. Machine learning algorithms like random forest and convolutional neural networks (CNNs) have been used to detect pollution hotspots, disease vector mapping, and resource allocation optimally in environmental management. Physics-based models integrated with ML approaches have been successful in improving the prediction and understanding of intricate environmental processes. Nevertheless, the integration of GIS, AI, and ML in environmental health monitoring is hindered by factors like data availability, quality, computational complexity, and ethics. The integration of GIS, AI, and ML is a paradigm shift in environmental health monitoring, allowing stakeholders to apply targeted interventions and improve resilience and sustainability against increasing environmental challenges.