Fault in Air Handling Unit (AHU) of the Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings is a challenge that building managements face. These faults cause buildings to waste 15 – 30% of the energy consumed by the AHU. This thesis aims to study the causes of faults in the AHU and proposes a machine learning model that could be used to detect these faults. These faults could either be failure of AHU equipment, failure of the actuator, or failure of sensor and feedback controller. To achieve this, the data driven method of fault detection was applied. Collected data was preprocessed by removing missing values, eliminating correlated features, nominal features were one-hot encoded, and class imbalance was managed by over-sampling and under-sampling techniques. Finally, Principal Component Analysis (PCA) technique was applied to the over-sampled dataset. Both over-sampled and under- sampled datasets were split by 70/30 ratio for train and test sets, respectively. Classification models were built using Random Forest, Decision Tree and Support Vector Machines for both binary and multiclass classifications. GridSearchCV cross validation technique was used to train the models and the optimal parameters for each model selected. Results from multiclass classifications, show that Random Forest performed best with over sampling and PCA having 100% on accuracy, 100% on precision, 100% on recall, and 100% on f1 score while with under-sampling without PCA, Support Vector Machines performed best with 91% on accuracy, 91% on precision, 91% on recall, and 90% on f1 score. This illustrates that machine learning could be used to detect faults in AHU with accuracy above 90%. Analyzing the results, the proposed machine learning models could detect the most important failure causes and the predictors of faults in AHU.