Machine Learning and Artificial Intelligence for Enhanced Software Engineering: Methods and Applications

The integration of artificial intelligence and machine learning techniques into software engineering practices has fundamentally transformed how we approach software quality assurance, system reliability, and information integrity. This paper surveys recent advances in AI-driven methodologies that address critical challenges in modern software systems, including false information detection in large language models, intelligent agricultural systems, distributed database optimization, and automated software testing. We examine how causal inference, graph neural networks, and multi-modal learning frameworks contribute to building more robust and reliable software systems. Our analysis demonstrates that combining domain-specific knowledge with advanced AI techniques enables the development of smart systems capable of addressing complex software engineering challenges across diverse application domains. This survey highlights emerging trends and provides insights into future research directions for AI-enhanced software engineering.