Wind turbines suffer from mass imbalance due to manufacturing, installation, and severe climatic conditions. Condition monitoring systems are essential to reduce costs in the wind energy sector. Many attempts were made to improve the detection of faults at an early stage to plan predictive maintenance strategies, but effective methods have not yet been developed. Artificial intelligence has a huge potential in the wind turbine industry. However, several shortcomings related to the datasets still need to be overcome. Thus, the research question developed for this paper was “Can data augmentation and fusion techniques enhance the mass imbalance diagnostics methods applied to wind turbines using deep learning algorithms?” The specific aims developed were: (i) to perform sensitivity analysis on classification based on how many samples/sample frequencies are required for stabilized results; (ii) to classify the imbalance levels using Gramian angular summation field and Gramian angular difference field and compare against data fusion; and (iii) to classify the imbalance levels using data fusion for augmented data. Convolutional neural network (CNN) techniques were employed to detect rotor mass imbalance for a multiclass problem using the estimated rotor speed as an input variable. A 1.5-MW turbine model was considered and a database was built using the software turbsim, fast, and simulink. The model was tested under different wind speeds and turbulence intensities. The data augmentation and fusion techniques used along with CNN techniques showed improvement in the classification and hence the diagnostics of wind turbines.