Growing Supply Chain (SC) complexity, demand volatility, and environmental pressures have increasingly limited the effectiveness of traditional static and non-intelligent network design approaches. These limitations are particularly evident in multi-period settings characterized by uncertainty, where adaptive, data-driven decision mechanisms are required. This study develops an integrated framework for designing a multi-period stochastic bi-level SC network to improve operational efficiency, enhance resource-allocation flexibility, and minimize total network costs. First, an enhanced Constrained K-Means algorithm is developed to dynamically reconfigure service zones over time while accounting for capacity restrictions and cluster-balance requirements. Subsequently, an Artificial Neural Network (ANN) classifies products according to their physical and functional characteristics and determines whether they should be routed through the central warehouse or cross-docking centers. The outputs of these two data-driven modules are incorporated as structured inputs into a bi-level stochastic optimization model that jointly addresses location, allocation, and routing decisions under uncertainty. In addition, a dynamic cluster-improvement algorithm iteratively adjusts cluster configurations based on shortage rates, thereby strengthening network responsiveness and resilience. The proposed framework is evaluated through a real-world case study. The numerical results indicate cost reductions of up to approximately 32% in specific periods, together with more stable resource utilization and improved overall SC performance.