This paper investigates the feasibility of estimating the daily peak load of the district heating system in Ulaanbaatar city using artificial intelligence algorithms. The proposed approach utilizes outdoor air temperature and historical load data from previous days as key input parameters. The results demonstrate that AI-based methods can effectively predict daily peak loads, providing a valuable tool for improving the operational efficiency and planning of urban heat supply systems. The feasibility of predicting the daily peak load of the district heating system in Ulaanbaatar city using artificial intelligence algorithms is examined. The prediction model incorporates outdoor air temperature and historical load performance as key input variables. In Ulaanbaatar city, thermal energy consumption in the district heating system is primarily attributed to heating, domestic hot water, and ventilation. These demands are strongly influenced by seasonal variations and weather conditions, particularly outdoor air temperature, which serves as the dominant factor due to its direct impact on residents heating demand.