Machine Learning in Computer Science: Algorithms, Architectures, Applications and Emerging Research Trends

This paper reviews machine learning algorithms, architectures, and applications, highlighting recent advances in deep learning, federated learning, edge AI, and automated ML. It also addresses challenges like data bias and interpretability, and explores emerging directions such as explainable AI and quantum machine learning.

A. Pawar· International Journal For Multidisciplinary Research· 2026· DOI 10.36948/ijfmr.2026.v08i02.75254

The core problem

Machine learning (ML) has become a cornerstone of modern computer science and artificial intelligence. It enables computer systems to learn patterns from large datasets and improve performance without explicit programming. Recent developments in deep learning, federated learning, edge AI, and automated machine learning have expanded the capabilities of ML systems across industries such as healthcare, finance, cybersecurity, and intelligent transportation. This paper provides a comprehensive review of machine learning algorithms, architectures, applications, and recent research developments from 2023-2025. The paper also discusses challenges such as data bias, computational complexity, and interpretability, and explores emerging research directions including explainable AI, quantum machine learning, and sustainable AI systems.

The paper conducts a comprehensive review of machine learning algorithms, architectures, applications, and recent research developments from 2023-2025. It synthesizes findings from existing literature to provide an overview of the current state of the field. The review covers a range of ML paradigms, including deep learning, federated learning, edge AI, and automated machine learning. The methodology involves analyzing recent advancements and identifying key challenges and emerging trends. The paper also discusses the implications of these developments for various industries and research directions.

Introduction

Machine learning (ML) has become a cornerstone of modern computer science and artificial intelligence. It enables computer systems to learn patterns from large datasets and improve performance without explicit programming. Recent developments in deep learning, federated learning, edge AI, and automated machine learning have expanded the capabilities of ML systems across industries such as healthcare, finance, cybersecurity, and intelligent transportation. This paper provides a comprehensive review of machine learning algorithms, architectures, applications, and recent research developments from 2023-2025. The paper also discusses challenges such as data bias, computational complexity, and interpretability, and explores emerging research directions including explainable AI, quantum machine learning, and sustainable AI systems.

The paper discusses the challenges facing machine learning, including data bias, computational complexity, and interpretability. Data bias can lead to unfair or discriminatory outcomes, particularly in sensitive applications like healthcare and finance. Computational complexity limits the scalability of ML models, especially with the growing size of datasets and model parameters. Interpretability is crucial for building trust and ensuring accountability, especially in high-stakes decisions. Emerging research directions aim to address these challenges. Explainable AI (XAI) seeks to make ML models more transparent and understandable. Quantum machine learning explores the potential of quantum computing to accelerate ML algorithms. Sustainable AI systems focus on reducing the energy consumption and environmental impact of ML. These directions are expected to shape the future of ML research and applications.

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