Last-mile delivery in e-commerce logistics is crucial and difficult, affecting consumer happiness and operationalefficiency. Fulfillment centers use delivery area/zone marking to ease this operation. This study examinesfulfillment center methods for optimizing last-mile delivery orders.This research first examines deliveryarea/zone labeling methods. These methods break geographical regions into smaller manageable parts forresource allocation and route optimization. Grid-based zoning, distance-based tagging, and contemporarymachine learning methods for dynamic and adaptive zone identification will be investigated.The study thenexamines delivery area tagging implementation factors. Zone tagging success depends on population density,order frequency, traffic patterns, and delivery time windows. Emission regulations and sustainability targetswill also be examined.Delivery area/zone tagging technology and tools are also examined. GPS tracking, GISmapping, and real-time data analytics enable effective monitoring and modifications. IoT devices andpredictive analytics will also be assessed for their impact on delivery performance.This study concludes withthe benefits and drawbacks of delivery area/zone labeling. Delivery time, operational expenses, and customerexperience improve. Fulfillment focuses face data privacy, algorithmic biases, and system scalability issues.Inconclusion, this study examines fulfillment center delivery area/zone labeling for last-mile delivery orders. E-commerce and logisticsstakeholders may maximize last-mile delivery by knowing the different methods,technology, and factors affecting them.