<p>Data assimilation plays a crucial role in enhancing rainfall prediction, where patterns of rainfall can change rapidly, particularly in regions prone to monsoons or tropical storms. Data assimilation updates models in near real-time, allowing them to adapt to changing atmospheric conditions. Conventionally, a multiple number of deep learning and machine learning frameworks are developed to predict rainfall but their inefficiency to mitigate the overfitting issues, and local convergence problem affected the interpretability and reliability of the rainfall prediction phenomena with increased error rate and computational complexity. Therefore, the research proposes a quest search optimization enabled multihead error minimum learning based deep neural network and recurrent neural network (QuSrO-MEnDRN) model for effective rainfall prediction. The QuSrO-MEnDRN model potentially yields higher prediction performances owing to the deployment of a modified deep spatial transformer-based U-Net (MDST-UNet) model for data assimilation, which involves estimating the actual state of a physical system to enhance reliability. Integrating the proposed QuSrO algorithm solely captures the optimal solution to tune the hyperparameters, thus enabling higher convergence with minimal prediction errors. Fusing sophisticated methods within the proposed model improves generalization ability, further revealing a minimum of 0.04 root mean squared error and 0.03 mean absolute error rates according to month analysis, compared to other conventional methods over rainfall prediction.</p>