Software engineering, along with the incorporation of Artificial Intelligence (AI), has emerged as a new technological vantage point that has permanently changed classical development practices and processes for any phase and aspect of the software lifecycle. In particular, this systematic literature review, which includes 135 peer‐reviewed papers extracted from the years 2010 to 2025, follows PRISMA guidelines. It examines modern instances of AI‐based requirements analysis, automated code transformation, predictive system modeling, proactive fault monitoring and detection, and advanced project guidance systems. Technologies can be powerful tools for increasing productivity and effectiveness and strengthening the quality of software development while making technology more complex—technologically, organizationally, and ethically. The generalization, explainability, privacy and algorithmic bias challenges of the model are discussed in detail. This paper shows how AI is helping companies to predict defects, automatically identify errors and optimize the software development. It also highlights the significant adoption barriers to these technologies for organizations. The review combines new industry research with existing practice to offer practical guidance on how these implementation challenges can be overcome and the ethical use of AI can be promoted. In contrast to existing reviews concentrating on isolated stages, the study offers an integrated review through life phases, distinctive ethical frameworks and a roadmap for adoption. Takeaway: Sustainable AI deployment in SE needs interdisciplinary collaboration, ethical oversight, and a mixture of guidelines to balance technology efficiency with responsibility. The paper highlights that interdisciplinary cooperation and ethical framings are requirements to integrate AI into software engineering in a sustainable, straightforward way. This review can be utilized as a guide for authors, scientists/practitioners, and policymakers in articulating the intellectual‐practical gap.

Software defect prediction is a critical task in software engineering, enabling organizations to proactively identify and address potential issues in software systems, thereby improving quality and reducing costs. In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. The models were trained and tested on preprocessed and feature‐selected data, followed by optimization through hyperparameter tuning. Performance evaluation metrics were employed to analyze the results comprehensively, including classification reports, confusion matrices, receiver operating characteristic–area under the curve (ROC‐AUC) curves, precision–recall curves, and cumulative gain charts. The results revealed that XGBoost consistently outperformed other models, achieving the highest accuracy, precision, recall, and AUC scores across all metrics. This indicates its robustness and suitability for predicting software defects in real‐world applications.