In recent years, advances in machine vision and artificial intelligence (AI) have paved the way for innovative applications in various domains. This study introduces an artificial intelligence-based prayer posture classification system that uses YOLOv5 to detect and classify Muslim prayer postures from multi-source data. The system is trained on 7630 images of different contexts and environments, showing the cultural diversity of prayer posture. The system employs multi-source data integration to distinguish clothing, lighting, and cultural practices. The system uses annotations to label the prayer postures in each image, achieving a high classification accuracy of 91.40% on a test set while considering four classes of prayer postures such as Qiyam, Ruku’, Sujud, and Jalsa. The proposed prayer posture detection system can be used in religious schools to teach proper prayer postures. It can also be used by individuals at home to practice and perfect their postures. This research work demonstrates the harmony between spirituality and science.