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

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. 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. 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.