This chapter discusses the applications and challenges of implantable devices for heart failure (HF) monitoring, as well as their prospects and limitations, and lists some of the data sets recently collected from wearable devices. Implantable devices are extensively used to monitor patients with cardiovascular diseases. Patients with a history of HF are monitored within the clinic or hospital and remotely using implantable cardiac devices. Wearable devices typically, however, focus on predicting HF or cardiac arrhythmia with the help of clinicians. There is a limited number of devices available that can be used to automatically detect a clinical abnormality of the patient reliably without human intervention. The chapter addresses the application of machine learning for detecting HF using wearable devices and its prospects. Machine learning approaches can also be utilized to identify HF patients at high risk for additional comorbidities or in remote HF monitoring systems to improve HF clinical outcomes.