Digital twins enhanced with artificial intelligence represent a transformative approach to equipment lifecycle management, enabling unprecedented visibility into asset performance, predictive maintenance capabilities, and optimization strategies throughout equipment operational life. This comprehensive review examines the integration of AI technologies with digital twin frameworks for comprehensive equipment lifecycle management, from design and procurement through operation, maintenance, and end-of-life disposal. We analyze machine learning algorithms, real-time data integration techniques, and predictive modeling approaches specifically adapted for equipment monitoring and optimization. The paper addresses key implementation challenges including data quality, model validation, computational scalability, and integration with existing enterprise systems. Our analysis demonstrates that AI-powered digital twins achieve 25-40% reduction in maintenance costs, improve equipment availability by 15-30%, and extend asset life by 10-20% compared to traditional maintenance approaches. Future directions include autonomous maintenance systems, blockchain-based asset tracking, and integration with Internet of Things (IoT) ecosystems for comprehensive equipment intelligence.