The pharmaceutical supply chain is an intricate and indispensable domain that necessitates precise demand forecasting, optimized inventory management, and transparent logistics to guarantee the timely availability of essential medications while mitigating costs. Conventional forecasting methodologies, such as statistical time-series models like ARIMA, have been extensively utilized but often exhibit limitations in capturing abrupt demand fluctuations and intricate long-term dependencies. Recent breakthroughs in machine learning (ML) and deep learning (DL), particularly Long Short-Term Memory (LSTM) networks, have markedly enhanced predictive accuracy. Furthermore, disruptive technologies such as blockchain and the Internet of Things (IoT) have fortified supply chain integrity by ensuring end-to-end traceability, counterfeit mitigation, and real-time pharmaceutical logistics oversight. This study capitalizes on these advancements by synergistically integrating AI, IoT, and blockchain within a cohesive pharmaceutical supply chain framework. We propose an LSTM-driven demand forecasting architecture that adeptly captures temporal dependencies, surpassing the predictive capabilities of conventional statistical models such as ARIMA, which is employed for comparative evaluation. ETL pipelines and API-driven automation facilitate real-time data acquisition from ERP systems, hospital repositories, IoT sensors, and market intelligence, thereby fostering a comprehensive, data-driven decision-making paradigm. Additionally, AI-augmented risk management mechanisms enhance strategic inventory control by preempting stockouts and mitigating surplus inventory accumulation. Blockchain integration ensures supply chain provenance, fortifies regulatory compliance, and eradicates counterfeit drug circulation. Moreover, the system incorporates automated alerts for potential inventory shortages, logistical disruptions, and regulatory non-compliance, enabling proactive intervention and supply chain resilience. The proposed AI-empowered framework demonstrates superlative predictive accuracy, enhanced inventory optimization, and fortified security via blockchain-enabled transparency. Empirical findings underscore the efficacy of our approach, exhibiting superior stock utilization, cost efficiency, and systemic robustness. This research contributes to the evolution of a cognitively intelligent, secure, and autonomous pharmaceutical supply chain, ensuring precision, efficiency, and reliability in global medicine distribution.