Intelligent Infrastructure: ML-Driven Approaches for Modern Software Engineering

Abstract

The growing complexity of software systems and the demand for rapid, reliable deployment have necessitated a shift from traditional infrastructure management to intelligent, adaptive solutions. This study explores the integration of machine learning (ML) techniques into modern software engineering workflows to develop intelligent infrastructure capable of autonomous optimization, predictive maintenance, and dynamic scaling. Using a mixed-method approach, the research analyzes data from 30 industry projects across sectors such as fintech, healthcare, and cloud services. The implementation of ML models including Random Forest, Gradient Boosting, Autoencoders, and Reinforcement Learning agents was evaluated using performance metrics like accuracy, latency, and F1-score, as well as operational KPIs such as MTTR, MTBF, and deployment frequency. Statistical analyses, including regression modeling and significance testing, reveal that ML integration significantly improves system reliability, reduces recovery time, and increases deployment efficiency. Sector-specific trends and practitioner feedback further support the scalability and human-centric benefits of ML-driven infrastructure. The findings suggest that intelligent infrastructure not only enhances technical performance but also fosters greater developer trust and usability. This research provides a comprehensive framework for engineering future-ready software systems, establishing machine learning as a cornerstone of intelligent, scalable, and self-optimizing infrastructure.