Urban Logistics is a rapidly evolving sector, and one of the main problems is the inefficiency of last-mile delivery. Our research attempts to solve the last-mile delivery challenges, preferably in the consumer goods industry, targeting larger sets consisting of tens to hundreds of thousands of consumers. The efficiency of last-mile delivery is largely impacted by the dynamic nature of population demographics and the resulting changes in demand across different localities, cultures, and time-frames. To address this issue, we introduce a novel approach to dynamic decomposition of geographical locations based on community detection using the population density, tailored to the variability in consumer demands. We utilize a density metric to cluster different units (localities) consisting of different numbers of consumers. Using population density, we facilitate the efficient assignment of delivery zones. Our approach also focuses on adapting delivery services to accommodate fluctuating demands throughout the day using heuristic methods followed by community detection. In addition, we validated our advanced last-mile delivery algorithm that anticipates dynamic last-mile delivery with traditional one-to-one assignment algorithms. We have carried out numerous experiments on randomly generated data and scaled them to reflect real-world scenarios, showcasing the potential of our research to significantly enhance the efficiency and responsiveness of last-mile delivery operations in the consumer goods industry.