A credit card is a widely used payment method for purchasing goods and services. Banks offer credit cards with many banking facilities to attract clients. However, credit card debt accumulation and payment delinquency have become major issues in recent years. It might result in the unauthorized use of someone else’s credit or debit card information to make fraudulent purchases or obtain cash advances. Credit card fraud may be achieved in a wide spectrum of ways, such as phishing, physical card theft, skimming, online payment systems hacking, or database infiltration. To address this challenge, this study focuses on credit card identification using client history data to assess the likelihood of payment. Four algorithms (Decision Forest (DF), Naïve Bayes (NB), Logistic Regression (LR), and Neural Network (NN) were evaluated to build a credit card assessment model. Results indicate that the Decision Forest algorithm achieved the highest accuracy (94.8%), precision (96.6%), and recall (92.4%) in identifying credit card clients likely to make payments. This study offers insights into credit card risk assessment and provides a foundation for developing effective credit card payment management strategies.