Coiled tubing (CT) units operate at higher speeds compared to conventional drilling rigs, while offering lower operational costs and more compact equipment. Additionally, they require less time for rig-up and provide greater overall efficiency. Consequently, CT technology has found extensive application in the oil and gas industry. Nevertheless, CT is susceptible to fatigue failure due to cyclic bending stresses experienced on the reel and guide arch, particularly under high-pressure operating conditions. The fatigue life of CT can be evaluated through three main approaches: the running foot method, the laboratory (trip) method, and the theoretical method. However, due to the inherent limitations of the running foot and laboratory methods, the development and application of predictive fatigue life models have become increasingly important. The present study aims to estimate the fatigue life of CT using artificial intelligence (AI)–based modeling techniques. For this purpose, 249 datasets were compiled from previously published studies and research articles. Three distinct machine learning algorithms—Support Vector Regression (SVR), Random Forest, and CatBoost—were employed to develop predictive models for CT fatigue life as a function of the number of trips. The primary objective was to identify the most effective algorithm for accurate fatigue life prediction. The results obtained from evaluating the three machine learning models indicated that their performance on the test dataset was very close to each other. This finding demonstrates the convergence of the models in learning the data patterns and the proper consistency between the input variables and the target output. Among these, the SVR model achieved the highest R² and the lowest AAPE and RMSE values, demonstrating superior performance. The other two models also exhibited satisfactory accuracy.