MACHINE LEARNING ALGORITHMS AND THEIR APPLICATIONS IN SOFTWARE COMPANIES: A CONCEPTUAL REVIEW

Abstract

Machine learning (ML) has emerged as a transformative force in the software industry, revolutionizing how companies design, develop, and deploy products and services. The integration of ML algorithms into the software development lifecycle (SDLC) has enabled automation, enhanced decision-making, and innovative features such as intelligent recommendations, anomaly detection, natural language processing, and predictive maintenance. As data-driven approaches become increasingly central to competitiveness, software companies are investing heavily in ML to optimize performance, personalize user experiences, and streamline operations. This research paper presents a comprehensive exploration of ML algorithms applied across various stages of software development and business processes. It categorizes algorithms into supervised, unsupervised, and reinforcement learning frameworks, highlighting their respective roles in product innovation, process optimization, and system intelligence. Additionally, the paper examines key challenges such as data governance, scalability, model interpretability, and ethical AI practices. By analyzing contemporary case studies and industry applications, the paper underscores the importance of MLOps, explainability, and responsible AI deployment in modern enterprises. The findings emphasize that sustainable ML adoption depends not only on technological advancement but also on robust data infrastructure, ethical considerations, and cross-functional collaboration between engineers, data scientists, and policymakers.