Renewable energy research in India is critical to providing energy security, reducing reliance on fossil fuels, and achieving long-term economic and environmental improvement. Precise stock price prediction in the renewable energy sector is crucial for optimizing investment choices, improving market returns, and facilitating India’s shift to a clean energy economy. The nation aims to become a global leader in renewable energy, with forecasting models serving a vital function in directing investors, enterprises, and government towards sustainable development. This study assesses Elastic-Net regression models for NHPC, Reliance Industries, Tata Power, and Borosil Renewable, considering volatility, market capitalization, and data limitations. Our results indicate that Forecast Distance Modeling with Differencing (FDM + Diff) enhances accuracy, whereas hybrid alpha regularization without differencing is effective for NHPC’s smaller dataset. These findings underscore the significance of customized models for optimized resource allocation in India’s growing renewable sector. India’s leadership in clean energy will be enhanced by strategic forecasting, which will accelerate the transition, reduce financial risk, and motivate global sustainability initiatives.