Cold chain logistics plays a critical role in ensuring the quality and safety of temperature-sensitive products such as food, pharmaceuticals, and agricultural goods. However, accurate demand forecasting remains a major challenge due to demand variability, perishability, and operational complexities. This study examines the impact of Artificial Intelligence (AI) on demand forecasting performance in cold chain logistics. The research adopts a quantitative approach using primary data collected through a structured questionnaire from professionals involved in cold chain operations. Key variables such as AI adoption, data quality, real-time data usage, investment in AI, employee training, and coordination among supply chain partners were analyzed using multiple regression techniques. The results reveal that the regression model is statistically significant, with AI-related factors explaining a substantial proportion of variation in forecasting performance. Among the variables, data quality and AI adoption emerged as the most influential factors, followed by coordination and real-time data usage. The findings indicate that AI-driven forecasting significantly improves accuracy, reduces forecasting errors, minimizes stockouts, and lowers wastage. The study concludes that effective integration of AI, supported by high-quality data and organizational readiness, can enhance operational efficiency and decision-making in cold chain logistics. The research contributes to the growing body of knowledge on AI applications in supply chain management and provides practical insights for industry practitioners aiming to improve forecasting performance.