The pharmaceutical cold chain sector in the United States brings in $100 billion in revenue but temperature violations cost $50 billion a year and endanger patient safety. Machine learning (ML) is studied in this research to boost both compliance rates and quality performance in cold chain logistics. Our study using both stakeholder interviews and Narrative case study observations on MediCool distributor alongside Random Forest and LSTM Model evaluations demonstrated how the ML approach decreased temperature deviations by 25% at the same time it cut expenses by 20% and exceeded 98% of FDA compliance standards for a period of twelve months. The approach enables 150 million Americans to obtain equal drug access due to its integration of IoT sensors, agile workflows, and human-centered dashboards. The proposed framework targets mid-sized companies by breaking down training limitations and outdated systems to advance AI capabilities which strengthen U.S. healthcare quality and fairness.

Machine Learning, Cold Chain Logistics, Pharmaceutical Supply Chain, Random Forest, LSTM, IoT, FDA Compliance, Patient Safety, Equity, Human-Centered Design

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