The forecast of near real-time water availability in reservoirs helps in effective water resources management, flood control, hydropower generation, irrigation, drought mitigation and climate analytics. This study uses learning techniques to forecast reservoir water availability based on historical water availability, hydro-meteorological data, and water demand. Three deep learning architectures have been used fully connected deep neural network, long short-term memory network, and WaveNets. They take inputs as storage, inflow, outflow, and precipitation leading up to the reservoir and use these inputs to predict the storage level 30, 60 and 90 days ahead. This study uses SHAP (SHapley Additive exPlanations) - a game theoretic approach to explain the output of the machine learning models. This predictive modelling exercise is complemented with hydrological modelling using Soil and Water Assessment Tool (SWAT) and the best model combinations are found. The visualization and dissemination of the results of this exercise and other important hydro-meteorological datasets is done through an interactive dashboard to help stakeholders in making informed decisions. The final product updates on daily input data and builds predictions and visualizations.