A Comprehensive Review of Machine Learning Algorithms for Big Data Analytics

Abstract

This study examines the role of machine learning algorithms in big data analysis, highlighting how their integration enables intelligent data processing and decision-making across diverse application domains. The continuous growth in data volume, velocity, and variety has made traditional data analysis techniques inadequate, thereby necessitating advanced analytical tools for efficient big data processing, analysis, and storage. Machine learning algorithms address these complexities by enabling automated pattern discovery, predictive modeling, and intelligent decision-making in large-scale and heterogeneous big data environments. This paper presents a structured review of machine learning algorithms for big data analysis, covering the background and characteristics of big data (5Vs) and examining supervised, unsupervised, and reinforcement learning techniques used in large-scale data analytics. The practical applications of these algorithms are discussed across multiple sectors, including healthcare, banking, retail, manufacturing, and telecommunications. The study further reviews emerging trends influencing the convergence of big data and machine learning, with particular emphasis on ethical artificial intelligence, data privacy, security concerns, and scalability issues. Key challenges such as model interpretability, scalability, data quality, and data diversity in big data analysis are critically discussed. Finally, the paper outlines future research directions, including real-time big data analytics, edge computing, automated machine learning, and ethical artificial intelligence frameworks, and presents a comparative tabular summary of machine learning algorithms applied in big data analysis to support structured understanding and future research.

Introduction

Big data refers to extremely large and complex datasets that traditional data-processing systems cannot efficiently handle. It is generated by humans, technology, and natural phenomena, and can be structured, semi-structured, or unstructured.

Key Points:

1. Types of Big Data: Big data is categorized based on its format and source, including structured, semi-structured, and unstructured data.

2. Characteristics (5Vs): Big data is defined by Volume, Velocity, Variety, Veracity, and Value, which describe its size, speed of generation, diversity, reliability, and usefulness.

3. Origin:

   • Data Explosion: Rapid digitalization led to unprecedented data growth from transactions, sensors, and social media.

   • Internet Growth: The rise of the web introduced vast amounts of structured and unstructured data.

   • Technological Advancements: Enhanced storage and processing capabilities allowed organizations to manage larger datasets, leading to the emergence of big data solutions.

4. Technologies: Big data processing is enabled by frameworks like Apache Hadoop and Apache Spark.

5. Applications: Big data is used across industries:

   • Healthcare: Personalized medicine, drug discovery, medical imaging.

   • Finance: Fraud detection, algorithmic trading, risk management.

   • Retail: Customer segmentation, inventory optimization, recommendation engines.

   • Manufacturing: Predictive maintenance, quality control, supply chain optimization.

   • Telecommunications: Network optimization, service personalization, IoT data management.

6. Challenges: Big data poses issues due to its volume, velocity, variety, and authenticity, requiring robust systems to process and secure it.

7. Future Role: Big data will drive advancements in:

   • AI and Machine Learning: Improving predictions, automation, and analytics across industries.

   • Data Privacy and Security: Enhancing encryption and compliance with regulations.

   • Smart Cities: Optimizing traffic, resource management, and urban planning.

   • Healthcare: Enabling precision medicine and population-level health analysis.

Machine Learning in Big Data: Machine learning is essential for analyzing complex big data, as it allows systems to automatically detect patterns, adapt, and improve without explicit programming, making it crucial for handling large, heterogeneous datasets efficiently.

Conclusion

Machine learning has emerged as a key enabler for extracting value from big data by supporting automated analysis, predictive modeling, and intelligent decision-making over large and diverse datasets. This paper reviewed the fundamental concepts of big data, discussed supervised, unsupervised, and reinforcement learning techniques, and examined how these methods are applied across domains such as healthcare, finance, retail, transportation, and smart systems. The review highlights the growing reliance on machine learning–driven analytics to handle data complexity and support data-driven operations in modern organizations. The main contribution of this study lies in providing a structured and consolidated overview of machine learning algorithms for big data analysis, supported by a comparative literature summary that links techniques with application areas. By identifying key research trends and practical challenges, this paper offers useful insights for researchers and practitioners seeking to design effective big data analytics solutions. Future work in this area is expected to emphasize real-time analytics, automated machine learning, edge-based processing, and responsible AI practices, further strengthening the role of machine learning in large-scale data analytics.

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Copyright

Copyright © 2026 Gyanendra Kumar Gautam, Vishesh Saxena, Vineet Mishra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.