Boiling Liquid Expanding Vapour Explosion (BLEVE) is a high-energy explosion that generates intense blast loads, posing significant safety risks to infrastructure and public safety. Accurate overpressure prediction is essential for developing effective safety measures and emergency response strategies. Traditional empirical models fail to capture complex nonlinear interactions, while Computational Fluid Dynamics (CFD) methods, though accurate, are computationally expensive and impractical for large-scale and real-time applications. This study develops a neural network-based machine learning model for BLEVE overpressure prediction, offering an efficient alternative to traditional approaches. To improve model interpretability and reliability, Shapley Additive Explanations (SHAP) is employed, identifying key features that influence BLEVE overpressure. Using SHAP insights, an optimized model is constructed by selecting the most significant features, enhancing both accuracy and generalizability. The proposed approach is validated on both FLACS simulation data and experimental datasets, achieving a Mean Absolute Percentage Error (MAPE) of 2.93% on simulation test data and 47.62% on experimental data—significantly outperforming empirical methods. By integrating explainability into the predictive framework, this model not only delivers superior accuracy but also provides critical insights into the underlying factors influencing BLEVE overpressure, making it a practical and trustworthy tool for explosion risk assessment.