In the nuclear fuel cycle (NFC) facilities, the failure of Heating Ventilation and Air Conditioning (HVAC) system starts with minor component failures and can escalate to affecting the entire system, ultimately resulting in radiological consequences to workers. In the field of air-conditioning and refrigerating engineering, the fault detection and diagnosis (FDD) of HVAC systems have been studied since faults occurring in improper routine operations and poor preventive maintenance of HVAC systems result in excessive energy consumption. This paper aims to provide a systematic review of existing FDD methods for HVAC systems therefore explore its potential application in nuclear field. For this goal, typical faults and FDD methods are investigated. The commonly occurring faults of HVAC are identified through various literature including publications from International Energy Agency (IEA) and American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). However, most literature does not explicitly addresses anomalies related to pressure, even though in nuclear facilities, abnormal pressure condition need to be carefully managed, particularly for maintaining radiological contamination differently within each zone. To build simulation model for FDD, the whole-building energy system modeling is needed because HVAC systems are major contributors to the whole building’s energy and thermal comfort, keeping the desired environment for occupants and other purposes. The whole-building energy modeling can be grouped into three categories: physics-based modeling (i.e., white-box models), hybrid modeling (i.e., grey-box models), and data-driven modeling (i.e., black-box models). To create a white-box FDD model, specialized tools such as EnergyPlus for modeling can be used. The EnergyPlus is open source program developed by US-DOE, and features heat balance calculation, enabling the dynamic simulation in transient state by heat balance calculation. The physics based modeling has the advantage of explaining clear cause-and-effect relationships between inputs and outputs based on heat and mass transfer equations, while creating accurate models requires time and effort. Creating a black-box FDD model requires a sufficient quantity and diverse types of operational data for machine learning. Since operation data for HVAC systems in existing nuclear cycle facilities are not fully available, so efforts to establish a monitoring system enabling the collection, storage, and management of sensor data indicating the status of HVAC systems and buildings should be prioritized. Once operational data are available, well-known machine learning methods such as linear regression, support vector machines, random forests, artificial neural networks, and recurrent neural networks (RNNs) can be used to classify and diagnose failures. The challenge with black-box models is the lack of access to failure data from operating facilities. To address this, one can consider developing black-box models using reference failure data provided by IEA or ASHRAE. Given the unavailability of operation data from the operating NFC facilities, there is a need for a short to medium-term plan for the development of a physics-based FDD model. Additionally, the development of a monitoring system to gather useful operation data is essential, which could serve both as a means to validate the physics-based model and as a potential foundation for building data-driven model in the long term.