Leveraging Machine Learning Models for Automated Product Design Optimization in Cloud Ecosystems

Jeremiah Folorunso1, Adetunji Oludele Adebayo2, Sopuluchukwu Ani3, Nathaniel Adeniyi Akande4, Uju Judith Eziokwu5
1Soft Alliance and Resource Limited, Nigeria
2Cybersecurity Professional/ Independent Researcher, University of Bradford, UK
3SAP Technical Consultant, Nigeria LNG Limited (NLNG), Nigeria
4Cybersecurity Analyst/Independent Researcher, University of Bradford
5Data Analyst/Independent Researcher, University of Bradford, UK
DOI – http://doi.org/10.37502/IJSMR.2025.81207

Abstract

Cloud ecosystems have become critical infrastructures for modern product design, enabling distributed collaboration, scalable computing, and real-time simulation. However, manual optimization of design parameters across these distributed systems remains a bottleneck, leading to inefficiencies in performance, cost, and innovation. This study explores how machine learning (ML) models can automate product design optimization within cloud environments. By leveraging predictive algorithms such as Bayesian optimization, neural networks, and reinforcement learning, the research demonstrates how ML can accelerate iterative design processes and resource allocation across cloud-based product development platforms. Using data-driven experiments simulated in AWS and Google Cloud environments, results show that ML-driven optimization achieved up to 31% improvement in design accuracy and 27% reduction in computational cost compared to traditional optimization methods. The study concludes that integrating intelligent ML models into cloud product design pipelines can significantly enhance innovation, reduce time-to-market, and ensure sustainable performance in global design ecosystems.

Keywords: machine learning, cloud ecosystems, product design optimization, Bayesian learning, reinforcement learning, digital manufacturing, automation.

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