The paper in hands presents a Long Short-Term Memory (LSTM) model to forecast solar power generation and management, as it aiming to improve the reliability and efficiency of solar energy systems. LSTM, a type of recurrent neural network, is well-suited for handling time series data and capturing long-term dependencies, making it an effective tool for predicting fluctuations in solar power generation due to variable weather patterns and seasonal changes. By utilizing historical weather data, irradiance levels, and past solar output, the LSTM model predicts short-term and medium-term solar power generation, allowing for optimized energy management and improved grid integration. This model helps address challenges in balancing demand and supply, reducing reliance on fossil fuels, and enhancing the sustainability of renewable energy sources. The results indicate that the LSTM-based forecasting model achieves high accuracy, significantly reducing prediction errors compared to traditional forecasting methods, thereby supporting more efficient solar power management strategies.