Predictive maintenance has become an essential approach in modern industries, utilizing AI and advanced modeling techniques, including physical and data-driven models, to address various equipment faults and failures that can negatively impact equipment, augment rentability then optimizing operations. This study explores various methodologies, evaluating their performance in terms of accuracy, efficiency, and robustness. This research represents the power of artificial intelligence and connected objects by transforming large quantities of data into actionable maintenance decisions, by using multiple algorithms, models and approaches, ranging from machine learning, deep learning, to hybrid approaches, NLP model for text data processing, and even generative models applying to maintenance. Starting with classification, comparing algorithms in terms of predictive accuracy, computational efficiency, and scalability, then discussing their real-world applications in decision making, this work contributes to a better understanding of AI and IoT and provides valuable insights into selecting appropriate strategies to enhance industrial maintenance through AI and connected systems.