Using the bibliometric analysis methodology, this study attempt to systematically review scholarly literature in the areas of customer satisfaction and machine learning. Scientific publications obtained from the Web of Science (WOS) database were retrieved using the keywords ‘customer satisfaction’ and ‘machine learning’. All relevant studies published up to 2025 were included into the scope of the review.

The quantitative distribution of the retrieved publication by year, the institutions and countries involved, the most cited articles, and the most frequently used keywords were evaluated with a detailed analysis. In this context, it can be concluded that the use of machine learning techniques in the assessment of customer satisfaction has significantly increased particularly in recent years.

Algorithms such as sentiment analysis, deep learning, random forest, and artificial neural network have been frequently applied to problems like classification, optimization, natural language processing, and text mining. This techniques are commonly used in areas such as the classification of customer complaints, sentiment analysis of reviews, and the prediction of consumer behavior.

The bibliometric analysis conducted in this study aims to provide a comprehensive overview of the current state of the literature. Furthermore, it highlights potential research directions for future studies. In this respect, the study serves as a reference for both researchers and practitioners helping them make more informed and data-based decisions.