Background: Rural infrastructure, agro-processing, and decentralized energy systems in the Osmanabad district of Maharashtra utilize a significant quantum of rotating machinery (e.g., centrifugal pumps, turbines, and compressors). Regular mechanical failures and erratic equipment breakdowns in these facilities result in substantial loss of productivity and maintenance problems, which can be particularly challenging in resource-poor settings with limited technical support. Objectives: The purpose of this work is to evaluate the effectiveness of AI-based PdM models in detecting faults and preventing machine malfunctions for rotating machinery. This paper aims to design context-sensitive, affordable, and understandable AI solutions that meet rural deployment requirements, to satisfy fault detection accuracy, maintenance cost savings, and stakeholders' trust. Methods: Employing a concurrent mixed-methods approach, the study integrated 6 weeks of multi-sensor data (vibration, temperature, acoustic signals) collected from five rural machinery sites in Osmanabad, with qualitative interviews with technicians and plant managers. Machine learning algorithms (CNNs, LSTMs, Isolation Forests, hybrid TCN-Autoencoders) were trained and validated under the supervised and unsupervised paradigms. The performance measures were the classification accuracy, mean squared error, and stakeholders' usability rating. Results: The fault detection accuracies were all higher than 95% for all the models. CNNs had the best performance with 99.89% for impeller blade faults, and LSTMs had 98.5% for turbine vibration anomalies. The total maintenance costs were decreased by 31% and the downtime was reduced by up to 70%. Technicians had high trust in AI systems, particularly if they were provided with explainable outputs such as fault heatmaps and predictive dashboards. Conclusions: AI-supported PdM systems are capable of generating impactful improvements in equipment reliability and operational efficiency when co-designed with community stakeholders and adjusted for a rural setting. This study adds to mechanical engineering and equitable AI adoption in underserved areas.