With a focus on fixing the common problems of imbalance and misalignment, this study introduces an artificial intelligence tool based on a state-of-the-art deep learning method that will enhance automatic condition monitoring and fault detection for mechanical processes. The main breakthrough is a trustworthy model for condition monitoring using artificial neural networks that extract feature vectors from signal data using frequency analysis. A high fault detection accuracy rate highlights the research accomplishment, proving its ability to establish new solutions also for predictive maintenance. This research considers the different working conditions of a mechanical process by analysing four separate operational classes, including balanced operation, horizontal-vertical misalignments, unbalanced situations and regular operation. The dataset studied in this work includes a wealth of information and was carefully calibrated for neural network training, which has also the potential to be employed in the development of maintenance procedures for mechanical plants. Finally, this study provides a significant step towards the goals of improved performance and unyielding safety requirements that industries are aiming for.