"International Journal of Geography, Geology and Environment"

The challenges of climate change impact greatly on environmental sustainability, where analytical methods are very strong to capture both the spatial variation and temporal changes of the climatic processes. This paper provides a unified framework of the study of the spatiotemporal climatic change based on the use of multi-source remote sensors and machine learning tools. Landsurface temperature land surface, vegetation indices and precipitation satellite measurements in 2000-2024 were joined with the latest machine learning models, such as Rapid Forest, Support Vector machine, Convolutional Neural Network, Long Short-Term Memory network. The models were tested on how well they predicted the climate variables, how they identified the hotspots of climate change as well as making long term predictions. The experiments have shown that deep learning methods perform better than conventional machine learning models, with a lower error of prediction and a greater power of explanation. Using the LSTM model, the lowest RMSE at 1.47 ^oC, MAE at 1.12 ^oC, and R² of 0.93 were obtained when predicting land surface temperatures, which is a reduction of about 30 percent compared to baselines used in the related research. The overall accuracy of CNN-based spatial analysis was 91.8 as it is highly capable of recording the presence of spatial heterogeneity. These findings prove that a combination of remote sensing with machine learning will contribute a lot to monitoring and prediction of climate change. The suggested framework offers a transferable and scalable framework to assist in the assessment of climate impacts, environmental management, and makes policy decisions based on data.