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The mortgage lending industry is experiencing a significant transformation as it embraces Artificial Intelligence (AI) for its impact on decision-making, operations, risk management, and customer service. Many of the activities associated with mortgage processes can be automated and improved using technologies such as ML, DL, NLP, computer vision and generative AI, as these tasks are often document-driven, rule-based, time-consuming, and heavily dependent on manual work. Credit risk assessment, automated underwriting, fraud detection, intelligent document processing, property valuation, default prediction, mortgage servicing and customer support are just some of the ways AI is being applied to the credit industry. The study also identifies trends that are emerging such as explainable and agentic AI systems, which could help in making better mortgage decisions. But there are several challenges with the implementation of AI, including data privacy and security concerns, algorithmic bias, poor data quality, regulatory compliance, unreliable model performance amidst shifting market dynamics, and others. There are future developments anticipated in explainable and responsible AI, privacy-preserving learning, generative and multimodal AI, adaptive governance, and more human-AI collaboration. In summary, if implemented appropriately, AI can enhance mortgage lending processes by making them more efficient and intelligent, while ensuring transparency, fairness, security, and good governance.