The Role of Machine Learning in Enhancing Data Science Workflows: A Systematic Review

Mudita  Sharma

doi:10.69968/ijisem.2025v4i1392-397

Authors

Mudita Sharma Research Intern, CSIR-CEERI, Jaipur, Rajasthan

DOI:

https://doi.org/10.69968/ijisem.2025v4i1392-397

Keywords:

Machine Learning (ML), Data Science Workflows, Natural Language Processing, Artificial Intelligence, Internet of Things (Iot), Data Analytics, Data Mining

Abstract

Big data is almost always the foundation of machine learning (ML) models, which have attracted a lot of interest in a range of applications, from computer vision to natural language processing. The intersection of data science, artificial intelligence, and software engineering is shown in the increasing number of products and applications that have integrated machine learning models. This review highlights that machine learning plays a pivotal role in enhancing data science workflows by automating complex tasks, improving predictive accuracy, and enabling data-driven decision-making. Proactive and reactive algorithms, supported by advanced computational power through GPUs and TPUs, allow better forecasting and response in real-world applications. Incorporating data engineering techniques with AI ensures scalability, efficiency, and reduced human errors. Furthermore, machine learning enables the discovery of hidden patterns, handling of massive datasets, and integration of smart computing for decision-making across domains such as business, healthcare, cybersecurity, and urban systems, thereby significantly advancing data science practices.

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