Introduction: My project, the Urban Heat Island Monitoring System, developed for the SDGs Hackathon at IIT Madras, utilizes Google Earth Engine (GEE) and machine learning to predict UHI values. Leveraging Earth Engine datasets, it employs Python, geemap, scikit-learn, pandas, and TensorFlow for data processing and model development. Key Components: Data Acquisition: GEE gathers LST, land cover, and urban morphology data. Machine Learning: Supervised algorithms, including deep learning, predict UHI values and analyze feature importance. Spatial Analysis: Fish grid methodology defines grid sizes, facilitating targeted analysis. Visualization: Matplotlib and Seaborn present UHI predictions and feature importance intuitively. Web Application: A user-friendly interface offers seamless access to UHI predictions and spatial analysis results. Conclusion: The Urban Heat Island Monitoring System empowers stakeholders with accurate predictions, aiding urban planning for sustainable development. Join me in addressing the challenges of urban heat islands and fostering sustainable cities and communities. #SDGs #UrbanHeatIsland #SustainableDevelopment #MachineLearning #GoogleEarthEngine