The importance of power transformers in electrical power systems cannot be overstated, as their failures can lead to considerable economic losses and disruptions. The typical malfunctions encountered by a power transformer comprise dielectric issues, thermal losses due to copper resistance, distortions in winding caused by mechanical faults, failure of bushings, malfunction of tap changers, core malfunction, tank malfunction, failure of the protection system, and failure of the cooling system. Traditional methods for transformer fault detection involve using the ratio of key gases present in the transformer oil when a fault occurs. These gases include Hydrogen (H2), Methane (CH4), Ethane (C2H6), Ethylene (C2H4), Ethyne (C2H2), Carbon Monoxide (CO) and Carbon Dioxide (CO2). For accurate and early detection of faults, traditional methods require complex algorithms. This project focuses on the predictive maintenance of power transformers using machine learning techniques, aiming to identify and address potential faults pre-emptively. By analysing various fault types and leveraging machine learning tech like Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbour (KNN), the project develops models that predict transformer failures based on historical data. Dataspell software and Python libraries such as Numpy and Matplotlib were used to train the model. The testing results showed the efficiency of the SVM, KNN, and Decision Tree methods in detecting the faults experienced by the power transformer. The testing accuracy for SVM, KNN and Decision Tree models was 95.65%, 95.65% and 89.13%, respectively. It was observed that the SVM and KNN models performed better than the decision tree model.