A Comprehensive Review of Modern Machine Learning Architectures: From Statistical Models to Adaptive Intelligent Computing Systems

Abstract

This research presents a comprehensive review of the evolution, performance, and limitations of modern machine learning architectures, spanning from classical statistical models to advanced adaptive intelligent computing systems. By systematically comparing diverse architectural families including linear models, tree-based learners, convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformers, and emerging adaptive systems the study evaluates their computational complexity, training efficiency, scalability, data requirements, interpretability, robustness, and adaptability. The findings reveal that while traditional models remain valuable for their simplicity and transparency, deep learning and Transformer-based architectures significantly outperform earlier methods in handling large-scale, high-dimensional, and unstructured data. However, these performance gains come with notable challenges, including high computational and energy costs, adversarial vulnerability, data bias, lack of explainability, and difficulties in deployment on resource-limited devices. The study also compares current results with key findings from the past decade, highlighting both continuities and major advancements in model capabilities, scalability, and reliability. Overall, the research contributes an integrated framework that synthesizes technical, ethical, and practical considerations, offering deeper insights into the strengths, limitations, and future directions of modern machine learning architectures. The study underscores the need for more interpretable, energy-efficient, and ethically aligned AI systems to support responsible and sustainable technological development.