This paper explores the transformative potential of artificial intelligence (AI) in optimizing the collection, integration, and analysis of equipment data for asset monitoring and maintenance. We delve into the technical aspects of how AI algorithms and architectures can streamline data acquisition from diverse sources, enable real-time data processing, and derive actionable insights for predictive maintenance. Case studies from manufacturing and aviation illustrate the tangible benefits of AI-driven data management in terms of improved equipment reliability, reduced downtime, and optimized maintenance costs. The paper also discusses key considerations for implementing AI solutions, including data quality, cybersecurity, and organizational change management. We present a comparative analysis of AI techniques, discuss the economic impact of AI-driven asset maintenance, and highlight the importance of human expertise in the loop. Finally, we outline future research directions at the intersection of AI, IoT, and blockchain for next-generation asset maintenance systems.