The primary challenge in the supply chain is minimizing travel distance and time between hubs and customers. Inappropriate assignment of vehicle routing results in travel distances longer than required, causing delays in achieving timely deliveries, and ultimately negatively affect the customer expectations routes. In this study, the selecting optimal vehicle routing has been addressed. This involves calculating the shortest possible that meets the demand effectively while adhering to various logistical constraints like warehouse fixed positions, demand variety, demand quantity, and destination locations. Dynamic programming has been developed where numerous time period-based fluctuations can be accommodated such as fluctuations in traffic, alternative routes availability, and changes in travel distances. The weighted demand cost matrix has been introduced to prioritize and cluster the group of customer nodes for the assignment of certain vehicles. Moreover, API google distance matrix (latitudes and longitudes) has been integrated into the model to extract live locations of source-and-demand nodes which are a function of different time periods of a day. The dynamic has optimized the vehicle routes and results in 30.9% reduction comparing the existing case. The validation was done through four more cases where different possibilities such as business expansion, network growth, demand fluctuations, and vehicle capacities.