The aim of this project is to create an effective machine learning model for the detection and forecast of Urban Heat Island (Urban Heat Islands) phenomenon in the mid region of Andhra Pradesh state specifically Vijayawada. High temperatures in urban areas relative to their rural areas are called Urban Heat Islands. The negative impacts include increasing energy use, health risks, and environmental destruction. The satellite imagery and Random Forest models, in particular, have a long-standing reputation of being inaccurate when it comes to geolocalization and even when time-based forecasts are provided, they are mostly misleading. Thus, this gives rise to inaccuracies and inconsistencies in hotspot identification and forecasting’s metrics. This project suggests an improved Recurrent Neural Network (RNN) model that incorporates Long Short-Term Memory (LSTM) algorithms, driven by the need for more precise and reliable predictions. The proposed LSTM-based model targets the traditional approaches shortcomings of being spatially and temporarily inaccurate in the detection of hotspots. The patterns of temperature, humidity, and soil moisture in city regions can be explained better by this model. It increases the model’s predictive capability and explains urban island’s patterns. The project uses data obtained through NASA/POWER CERES/MERRA2 Native Resolution Daily Data, which provides an extensive collection of temperature, humidity, and soil moisture records. These factors will be used to develop forecasting and predictive models of the Urban Heat Islands hotspots. Normalization is one of the methods employed even during advanced data preprocessing.