<p>Machine translation tools have gained increasing popularity among translation learners, transforming their translation learning behaviors. However, it is unclear how learners’ continuance intention to use these tools is affected yet. Based on the task-technology fit (TTF) model and the value-based adoption model (VAM), the present study examined the influence of technology characteristics, task-technology fit, performance, and perceived value on learners’ continuance intention. A total of 387 respondents participated in the survey questionnaire. The results of structural equation modeling revealed that: (1) Both technology characteristics and task-technology fit had a positively direct effect on performance; (2) Both performance and task-technology fit had a positively direct effect on perceived value; (3) Perceived value served as a key factor influencing learner’s continuance intention. Moreover, the Mann-Whitney U test revealed that: (1) male and female learners showed non-significant differences in performance, perceived value, and continuance intention; (2) learners within different majors exhibited significant differences in performance and perceived value, but non-significant differences in continuance intention; (3) learners with different usage intensity showed significant differences in performance, perceived value, and continuance intention. These insights offer valuable implications for educational practitioners and researchers in refining and promoting the effective use of machine translation tools in EFL contexts.</p>