Authors: Dr. Victoria S. Turner, Dr. Isabella N. Hughes, Dr. Christopher J. Walker, Prof. Daniel T. Harrison, Naveen Kumar
Abstract: Machine learning-driven infrastructure blueprinting and cloud architecture optimization represent a transformative approach to modern enterprise computing environments by integrating intelligent automation, predictive analytics, and adaptive resource management into cloud infrastructure design and deployment processes. Traditional infrastructure planning methods often require extensive manual intervention, static configuration models, and continuous monitoring efforts, which can lead to inefficiencies, increased operational costs, and scalability limitations in dynamic cloud ecosystems. This research explores the application of machine learning techniques in automating infrastructure blueprint generation, workload prediction, resource allocation, performance optimization, and fault detection across multi-cloud and hybrid cloud environments. By leveraging supervised learning, reinforcement learning, and deep neural networks, intelligent systems can analyze historical operational data, identify optimal architectural patterns, and generate scalable infrastructure configurations that align with business requirements, security policies, and compliance standards. The study further examines how AI-driven optimization improves cloud elasticity, reduces energy consumption, enhances infrastructure reliability, and accelerates Infrastructure as Code (IaC) deployment workflows through automated decision-making and self-healing capabilities. Additionally, the research highlights the integration of predictive analytics for proactive capacity planning, anomaly detection, and cost-aware cloud orchestration to improve operational resilience and service availability. The findings demonstrate that machine learning-enabled cloud architecture optimization significantly enhances deployment efficiency, reduces human error, strengthens infrastructure governance, and supports intelligent digital transformation initiatives in modern enterprises.
DOI: http://doi.org/10.5281/zenodo.20351605
