<p>In order to meet the growing demand for tailored prescription recommendations, the pharmaceutical sector must ensure the safe and effective management of drug supply chains, which presents considerable hurdles. This study provides an integrated system that uses cutting-edge Reflection equivariant quantum neural networks and lightweight smart contracts framework for blockchain technologies for Drug Supply Chain Management and Recommendations (DSCMR). To ensure safe drug delivery and combat counterfeiting, this manuscript provides a lightweight smart contract framework for a blockchain-based pharmaceutical supply chain management system. The technique use a Reflection equivariant quantum neural networks with White Shark optimization (REQNN-WSO) based recommendation system trained on a previously collected dataset of drug user reviews that included ratings and comments based on the users' medical conditions. Reviews and ratings from patients based on their experiences with different medications are collected and utilized as input for this work in the UCI Drug Review Dataset. In addition, preprocessing and analysis are done on the data to clean and make sense of it. After the data has been preprocessed, it is added to the Pure Transformer Network (PTN) features extraction phase for medication prediction. Reflection equivariant quantum neural networks (REQNN) are also capable of predicting and recommending drugs. The White Shark Optimization (WSO) method can be used to tune the hyperparameters of REQNN. The performance of the REQNN method is analyzed using the dataset and attains 99% accuracy, and 0.1% error rate, and the latency 17% and attains higher results compared with the previous methods.</p>