Synthesizing AI and Data-Driven Frameworks for Real Estate Lease Management

Authors -Dayanand Jamkhandikar

Abstract- – The integration of artificial intelligence (AI) and data-driven frameworks into real estate lease management offers transformative potential for optimizing decision-making and operational efficiency. This paper explores the synthesis of advanced AI techniques, including machine learning (ML) and deep learning (DL), with cloud-native architectures to automate lease abstraction, enhance data accuracy, and enable predictive analytics. By leveraging AI-driven models and data lakes, organizations can overcome traditional inefficiencies in lease management, such as manual processing and fragmented data systems. Building upon the foundational works of Ramakrishna Manchana on cloud-native solutions, event-driven architectures, and AI applications in real estate, this study proposes a comprehensive framework for real-time lease insights and actionable analytics. Key findings demonstrate significant cost reductions, improved compliance, and enhanced scalability, positioning AI as a critical enabler for modern property management.

DOI: /10.61463/ijset.vol.10.issue6.328