This study presents an innovative method utilizing weightless neural networks (WNNs) to identify and address various types of faults in air compressor modules. Random access memory (RAM) devices are harnessed by WNNs to emulate the functioning of neurons. The training process employs a versatile and effective algorithm aimed at generating reliable and accurate results. One notable benefit of employing WNNs is its ability to eliminate the necessity for network retraining and the generation of residuals. This feature makes WNN suitable for applications related to classification and pattern recognition. In this study, a specific type of air compressor, namely a single-acting single-stage reciprocating one, was chosen. Various potential faults like fluttering inlet and outlet valves, valve plate leakage, and check valve issues were taken into account. From the initial vibration data, statistical, histogram, and autoregressive moving average features were derived. For efficiency, the J48 decision tree algorithm was utilized to identify the pivotal features in this investigation. Following this, the features were divided into separate sets to evaluate the validation, training, and testing accuracies of the WNNs using the WiSARD classifier. Additionally, fine-tuning of hyperparameters was done to enhance classification accuracy while simultaneously reducing computational time. The results obtained demonstrate that, with the specified hyperparameter configurations, the WiSARD classifier attained an accuracy of 98.6667% for statistical features. The proposed method outperforms existing approaches, showing potential for real-time application in enhancing air compressor lifespan, reliability, and safety.