Assessing environmental impacts. These models assist in tasks such as maintaining water balance, simulating groundwater flow, and establishing protection zones. However, current models primarily rely on traditional lumped approaches, where groundwater is treated as a single unit without considering interactions with streams and aquifers. This limitation reduces their accuracy in estimating water availability and safe withdrawal levels. Our proposed system integrates advanced techniques, including Deep Convolutional Neural Networks (DCNN) and Random Forest (RF), to enhance groundwater level predictions. Predicting future groundwater levels is a complex task influenced by various factors, such as climate conditions, land use, water extraction rates, and natural recharge processes. Even in cases where historical groundwater data is unavailable (or where groundwater has not existed in a region), our model incorporates key influencing factors to predict the potential future presence of groundwater. To achieve this, we employ clustering algorithms, particularly Agglomerative hierarchal clustering, which groups states based on water availability and categorizing regions with low water levels separately from those with higher levels. This approach captures the complexities of groundwater systems, including recharge rates, stream interactions, and seawater intrusion. While traditional models have limited accuracy, our AI-driven method significantly improves predictive capabilities, providing a more reliable and data-driven approach to groundwater management in India.