Introduction: Heart failure (HF) poses a significant burden on public health. This can be largely attributed to recurrent hospitalizations in consequence of HF decompensation. Detection of early signs of impending fluid retention may facilitate timely medical intervention and thereby prevent hospitalizations. Monitoring of Cardiac Implantable Electronic Devices (CIEDs)-derived parameters has been proposed as promising solution, as the sensor inherent in CIEDs provide the ability to continuously monitor physiological signals. The aim of this study was to develop personalized machine learning (ML) models that can identify upcoming HF decompensation based on CIED-derived parameters. Methods: Two ML models, a support vector classifier (SVC) and an extreme gradient boosting (XGBoost) model, were developed for all patients. Features known to be associated to HF decompensation were extracted from daily CIED data. The output of the models is the daily classification of the patient’s HF status, either ‘stable’ or ‘unstable’. Model performance was evaluated through area under the precision-recall curve (AUPRC). First, the models were tested on a development dataset with leave-one-out cross-validation, and subsequently on an independent test set. Results: In total, for 62 patients two models were developed. The average AUPRC on the independent test set of the XGBoost models was 0.63 ± 0.28 and of the SVC models was 0.57 ± 0.26. Finally, for each patient, the model that resulted in the highest AUPRC was selected. The final models achieved an AUPRC on the independent test set of 0.61 ± 0.28. Conclusion: The findings of this study show promising results for the use of personalized CIED-derived models. However, significant variability in model performance across patients highlight the need for further research.