Cardiac rehabilitation (CR) focuses on the improvement of health or the prevention of further disease progression after an event. Despite the documented benefits of CR programs, the participation remains suboptimal. Home-based CR programs have been proposed to improve uptake and adherence. The goal of this study was to apply an end-to-end methodology including machine learning techniques to predict the 6-month adherence of cardiovascular disease (CVD) patients to a home-based telemonitoring CR program, combining patients’ clinical information with their actual program participation during a short familiarization phase. Fifty CVD patients participated in such a program for 6 months, enabling personalized guidance during a phase III CR study. Clinical, fitness, and psychological data were measured at baseline, whereas actual adherence, in terms of weekly exercise session duration and patient heart rate, was measured using wearables. Hierarchical clustering was used to identify different groups based on (1) patients’ clinical baseline characteristics, (2) exercise adherence during the familiarization phase, and (3) the whole program adherence, whereas the output of the clustering was determined using repetitive decision trees (DTs) and random forest (RF) techniques to predict long-term adherence. Finally, for each cluster of patients, network analysis was applied to discover correlations of their characteristics that link to adherence. Based on baseline characteristics, patients were clustered into three groups, with differences in behavior and risk factors, whereas adherent, non-adherent, and transient adherent patients were identified during the familiarization phase. Regarding the prediction of long-term adherence, the most common DT showed higher performance compared with RF (precision: 80.2 ± 19.5% and 71.8 ± 25.8%, recall: 94.5 ± 14.5% and 71.8 ± 25.8% for DT and RF accordingly). The analysis of the DT rules and the analysis of the feature importance of the RF model highlighted the significance of non-adherence during the familiarization phase, as well as that of the baseline characteristics to predict future adherence. Network analysis revealed different relationships in different clusters of patients and the interplay between their behavioral characteristics. In conclusion, the main novelty of this study is the application of machine learning techniques combining patient characteristics before the start of the home-based CR programs with data during a short familiarization phase, which can predict long-term adherence with high accuracy. The data used in this study are available through connected health technologies and standard measurements in CR; thus, the proposed methodology can be generalized to other telerehabilitation programs and help healthcare providers to improve patient-tailored enrolment strategies and resource allocation.