Band gap is a fundamental electronic property that governs the electrical and optical behaviour of materials and is widely used to classify them as conductors, semiconductors, or insulators. It plays a critical role in the design and application of materials for the semiconductor industry, electrocatalysis, solar cells, and photocatalysis. However, the exploration of band gaps if new materials through synthesis is impractical due to the vast chemical space of materials and the associated time and cost constraints. With advances in theoretical computation, ab initio calculations based on density functional theory (DFT) have emerged as an effective approach for predicting material properties from atomic structures, providing valuable preliminary insights prior to experimental validation. The predictive power of these methods can be applied to large datasets by integrating diverse theoretical descriptors and applying them within a high-throughput screening framework. In present work, we performed systematic data mining from the Materials Project database and constructed a general screening workflow that prioritizes both synthesizability and aqueous stability under electrochemical conditions relevant to the oxygen evolution reaction (OER, 0.787 to 1.587 VSHE) and hydrogen evolution reaction (HER, -1.213 to -0.413 VSHE). Following this pre-screening, accurate automated band gap calculations were carried out via DFT using hybrid Heyd−Scuseria−Ernzerhof (HSE06) functional. This integrated approach enables efficient identification of promising, experimentally viable materials and provides a robust strategy for high-throughput band gap screening in electrocatalytic and energy-related applications.