Centrifugal pumps play critical roles in industries such as oil and gas, water treatment, and power generation. Traditional pump maintenance approaches, whether reactive or preventive, are often inefficient, which results in unplanned downtime and increased operational costs. This chapter examines the application of artificial intelligence (AI) for predictive maintenance (PdM) of centrifugal pumps. It uses real-world vibration data to estimate the remaining useful life (RUL) and predict failures before they happen. A comprehensive literature review highlights the limitations of traditional methods, including acoustic emission, motor current signature analysis, and vibration analysis. It also presents the current trends in machine learning, edge computing, and explainable AI that allow for more informative and meaningful performance-based maintenance decisions. The methodology we used includes acquiring and preprocessing the time-domain vibration signals taken from six pumps of different health conditions. A new metric, DaysToFailure, is formulated to function as a surrogate target in the absence of real failure timestamps. For feature construction, a seven-day sliding window is used with average and range values of time-domain vibration values. A feedforward neural network was trained using Bayesian regularization to prevent overfitting. In our analysis, the mean squared error was reduced from 1400 to about 300, indicating a significant performance improvement. The model can provide a high predictive accuracy on healthy pumps and reasonable generalization in noisy and faulty conditions. Remarkably, the model can identify the impacts of the maintenance events based on the differences in the vibration occurring data, despite the lack of their explicit labels or timestamps, thus demonstrating itself to be suitable for practical industrial use. The results validate the notion that pump life can be elongated, unplanned failures can be avoided, and smart maintenance planning can be improved using an AI-based predictive maintenance structure. This research contributes to the development of scalable, interpretable PdM systems. These systems can be integrated into existing industrial monitoring infrastructures. Future studies are proposed for the fields of frequency-domain analysis, SCADA integration into PdM, and multi-sensor data fusion for PdM. With these new features, fault detection will be faster, less downtime will occur, and pump maintenance can be better organized based on more dependable and relevant information. As a result, companies will see better performance from their processes and cut down on the cost of repairs.