AI-Powered Defect Prediction: From Code Smells to Failure Forecasting

Manual flaw discovery becomes even more insufficient as software systems grow in complexity. From early signs like code smells to late-stage system failures, this systematic study investigates the use of artificial intelligence (AI) and machine learning (ML) approaches to anticipate software problems across several phases of the software lifetime. 500 peer-reviewed papers released between 2013 and 2025 were examined for techniques, datasets, and assessment measures using PRISMA guidelines. Important artificial intelligence models consist in Random Forest, SVM, deep learning architectures, and new transformer models. Features applied span static measurements, process-based indications, and textual data from code repositories. The paper exposes a developing tendency toward hybrid models, multimodal features, and an emphasis on explainability and cross-project adaptability. Generalizability, interpretability, and dataset consistency still present difficulties notwithstanding advances. Research gaps are highlighted in the study together with future prospects including explainable artificial intelligence, real-time CI/CD integration, and human-in- the-loop systems for strong and proactive software quality assurance.