This project explores the utilization of a Stacked Long Short-Term Memory (S-LSTM) model for estimating wind speed, which is crucial for sectors like renewable energy, weather forecasting, and environmental monitoring. Detailed wind speed estimation helps optimize wind turbine operations, improve power grid management, and mitigate risks from extreme weather. Stacked LSTM, an advanced variation of traditional LSTM, is effective for capturing complex temporal dependencies in time-series data due to its multi-layered structure. The objective is to measure the performance of the Stacked LSTM model and compare its predictive accuracy with other machine learning models, including standard LSTM, Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN). Performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Co-efficient of determination (R2) are utilized for this comparison. Stacked LSTM projects greater performance than other models, particularly in RMSE, MAE and R2 indicating its superior ability to model complex wind speed patterns. R2 also highlights the Stacked LSTM’s effectiveness, capturing a greater proportion of variance explained by the model compared to RNN, CNN, and traditional LSTM models. In conclusion, this project demonstrates that the Stacked LSTM model is highly effective for wind speed prediction, providing significant improvements over conventional models. Real-time data from National Renewable Energy Laboratory (NREL) in Muppandal of Kanyakumari, Tamil Nadu, further supports the model’s practical relevance for real-world applications.