ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN MODERN IT: TRANSFORMING THE FUTURE OF COMPUTING

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies that are reshaping modern information technology (IT) systems. This study investigates the role of AI and ML in enhancing computational efficiency, decision-making, and system performance across key IT domains, including cybersecurity, cloud computing, healthcare, and financial technologies. A quantitative and comparative research approach is employed, utilizing benchmark datasets and simulated performance metrics to evaluate multiple machine learning models such as Random Forest, Support Vector Machine, Logistic Regression, Deep Neural Networks, and XGBoost. The analysis incorporates standard evaluation techniques, including accuracy measures, confusion matrices, and Receiver Operating Characteristic (ROC) curves, to assess model effectiveness. The results demonstrate that advanced models, particularly XGBoost and Deep Neural Networks, achieve superior performance in terms of accuracy and predictive reliability, while traditional approaches show comparatively lower efficiency. Furthermore, the study highlights the impact of AI integration on reducing operational costs, improving automation, and enhancing scalability in IT systems. Despite these advantages, challenges such as data privacy concerns, implementation costs, and model bias remain significant. This research provides a comprehensive framework for understanding the application of AI and ML in modern IT and offers valuable insights for future technological advancements and decision-making strategies.