This study addresses the optimization of urban logistics paths by integrating multi-source information for enhanced decision-making in sustainable logistics. Recognizing the challenges posed by traditional methods, the research employs advanced data fusion techniques to analyse real-time logistics operations. Key contributions include the development of a multi-source information fusion framework utilizing the Extended Kalman Filter (EKF) and a performance evaluation based on accuracy, cost-efficiency, and environmental impact. Results highlight significant improvements in delivery routing precision, system adaptability, and cost reductions, offering valuable insights for logistics providers and policymakers striving for greener urban logistics systems.