Electric vehicles (EVs) are emerging as the future of carbon–neutral societies with the expectation of billions of dollars of investment and 35% of global average sales in the coming years. One of the survival challenges of EV-producing companies in international markets is the hesitancy and distrust of the buyers over the lifecycle of the EV batteries. This paper intends to develop an inventory model with various cost parameters to prevent the occurrence of failures of EV batteries with the strategies of conducting quality checks during and after the production process. The inventory model is environmentally conscious with costs of abating electronic wastes. The numerical example with secondary data is analyzed with different changes in the parametric values. As an extension part of the analysis, the learning algorithms are applied using MATLAB to design an optimal deep neural network model to represent the architecture of the proposed inventory model. The significant cost parameters are also subjected to ridge regression analysis to make an optimal forecast of optimal production quantity. The comparative analysis with different deep learning techniques is made to validate the model results. Key findings indicate that any increase in costs such as setup, raw material procurement, equipment upkeep, manufacturing, holding, inspection, quality sustenance, reworking, disposal, green initiatives, and waste treatment leads to an increase in the Total Average Cost (TAC). The results obtained from neural network analysis are validated through ridge regression, with the evaluation metrics demonstrating high accuracy. The proposed model is more promising, and this model shall be practically implemented to manage the production challenges of EV batteries.