Pharmaceutical Supply Chain Management (PSCM) encompasses a series of processes involved in the production of pharmaceutical products from the manufacturer to the end consumer. However, detecting and preventing intrusions on sensitive data on PSCM is a relentless challenge. To bridge these gaps, a novel Solana-Based Intrusion Detection Using Deep learning in Pharmaceutical Supply Chain Management (SID2S–CHAIN) framework is proposed for securing the pharmaceutical supply chain to resist from cyberattacks in the healthcare environment. Initially, the input data from the pharmacy are updated to the blockchain and all the entities in the supply chain has the synchronized and immutable record of the stock requirements. The supplier accesses the blockchain to retrieve the updated stock details and performs a data integrity check to ensure that the information from the blockchain is accurate. If any inaccurate information is sensed, those data are fed under the attack detection phase which is integrated with correlation-based pre-processing and combined Stacked Deep Learning (Stacked-DL) network-based data classification. Once verified, the supplier sends the required medicine information to the manufacturer to prepare the medicines and finally, all the requested medicines are packed based on the manufacturer’s instructions. The SID2S–CHAIN framework is evaluated by using IoT-23 dataset and it is simulated by using MATLAB. The experimental result shows that the accuracy of the SID2S–CHAIN framework has increased up to 90% for attack detection in PSCM. The accuracy of the SID2S–CHAIN framework achieves 5.55, 2.22, and 8.88% improvements compared to the PharmaChain, IMEFC, and BSFR-SH techniques, respectively.