The rapid development of digital technology and artificial intelligence has made the improvement and optimization of intelligent warehousing and automated distribution systems important topics for research in modern logistics management. With this as the background, the current study uses a systematic approach to explore critical factors, innovative ways, and implementation strategies related to these factors and their role in improving the effectiveness of intelligent warehousing systems. The study adopts a mixed-methodological approach, establishing a comprehensive evaluation index system including operational efficiency, technical performance, and economic benefits, and simultaneously verifying the implementation of the system through empirical analysis. According to the findings, the intelligent warehousing system increased the efficiency of operations in relation to order processing time and had reduced it by 71.7%, and enhanced the accuracy of picking to 99.8%. The intelligent warehouse system by use of machine learning and meta-heuristic algorithms had greatly improved the efficiency in resources utilization and energy as storage utilization increased by 19.3% while energy consumption dropped by 31.4%. A cost-benefit analysis shows that, despite the significant up-front financial investment, the system achieved a 186% return on investment over three years. This research deepens the theoretical understanding of intelligent warehousing and, at the same time, provides optimization strategies applicable to industry practice. Future research directions should focus on exploring the applications of multi-agent digital twin technology and researching how intelligent warehousing systems contribute to supply chain resilience and sustainability.