<p>Model predictive control is a powerful advanced process control technique for controlling dynamic systems with complex interconnections. It is a control strategy that forecasts future behavior using a dynamic model and then optimizes control actions to fulfill objectives while accounting for constraints. Heating, ventilation, and air conditioning (HVAC) is the process of regulating temperature, humidity, and air quality in a compact space using a range of technologies. Due to their significant energy consumption, HVAC systems require sophisticated control techniques and multi-objective optimization to maximize system efficiency and minimize costs. Building HVAC performance can be optimized with the help of model predictive control, which provides a strong framework that lowers energy expenditures, increases comfort, and improves overall system performance. The main focus of the paper is a detailed review of the model predictive control technique for heating, ventilation, and air conditioning systems. The fundamental concept, the basic principles, and the ongoing state of model predictive control research in heating, ventilation, and air conditioning are explained in this review. The difficulties with heating, ventilation, and air conditioning systems and control strategies are outlined, along with a modern comparison of several heating, ventilation, and air conditioning control strategies using model predictive control. The review examines and briefly discusses some of the key elements that influence model predictive control performance in the heating, ventilation, and air conditioning system. The review identifies several&#xa0;promising research areas and model predictive control implementation gaps in heating, ventilation, and air conditioning technology that warrant additional investigation. This review compiles important developments in the field and offers scholars and researchers insightful information.</p>