Shahid Rajaee Port, as the largest and most important commercial port in the country, serves as the main hub for containerized cargo exchange. In recent years, with the development of the container terminal as part of the expansion project of the west edge of basin No. 3 in Shahid Rajaee Port, to accommodate the largest seventh-generation container vessels with a draft of approximately 17 meters and the enhancement of the port's container capacity from 6 to 8 million TEUs, along with private sector investments in the supporting lands of the port, there is a growing need for reassessing the operational and terminal management practices at the container terminal of this port. Consequently, the optimization and improvement of the efficiency of various terminal facilities and supporting lands have become the focus of attention for operators and stakeholders in the port industry. Considering the projected increase in port operations and the demand for containers in Shahid Rajaee Port, using new technologies for optimization of the management of empty container operations for allocation to different terminals within the port, as well as allocation to vessels transferring empty containers to other ports in the country/world has been a focus. This article introduces an optimization process for empty container operations using machine learning and artificial intelligence methods. Given the possibility of predicting the demand for empty containers, it is possible to reduce the daily operation volume through advance planning and consider necessary measures regarding the appropriate spatial distribution of empty containers before the demand arises.