The global shift toward renewable energy is driven by the dual imperatives of rising energy demand and the need to reduce environmental harm caused by fossil fuels. However, renewables like wind and solar power pose unique challenges, particularly due to their intermittent generation and current limitations in energy storage technologies. Battery banks, commonly used to store surplus energy, degrade over time, making accurate forecasting of their remaining usable lifecycles critical for maintaining system reliability and efficiency. This study proposes a novel approach for forecasting battery health using an optimized long short-term memory (LSTM) network. To address the complexity of deep learning hyperparameter selection, a modified metaheuristic optimization algorithm is developed and integrated into a broader optimization framework aimed at improving model performance while minimizing overfitting. The method is benchmarked against several state-of-the-art optimizers, with results validated through comprehensive simulations and statistical analysis. This work contributes a scalable forecasting methodology, an effective optimization strategy, and interpretable results to support sustainable energy storage solutions.