The evolution of omnichannel retail has created unprecedented complexity in fulfillment operations, where retailers must simultaneously manage multiple channels while maintaining competitive delivery promises. This study examines how artificial intelligence (AI) and machine learning (ML) technologies can optimize fulfillment path design to minimize promise-time breaches while balancing operational costs and service levels. Through systematic analysis of current literature and industry implementations, we identify key algorithmic approaches including reinforcement learning, predictive analytics, and optimization algorithms that enable dynamic routing decisions. Our findings demonstrate that AI-driven fulfillment systems can reduce promise-time breaches by 15-30%, decrease operational costs by 10-25%, and improve customer satisfaction by 20-35% compared to traditional static routing methods. The research contributes to supply chain management theory by providing a comprehensive framework for implementing AI-driven fulfillment optimization in large-scale retail environments such as Walmart, grocery chains, auto care, and pharmaceutical retailers (PetRx). Practical implications include actionable strategies for retail executives seeking to modernize their fulfillment infrastructure and enhance omnichannel capabilities.