AIMS: Heart failure is an escalating burden on global healthcare systems. Modernizing heart failure care is inevitable, with eHealth products poised to play an important role. However, eHealth devices that can initiate and adjust heart failure medication are currently lacking. Consequently, this study aimed to develop an artificial intelligence-based decision engine to provide guideline-based recommendations for disease-modifying medication in heart failure patients. METHODS AND RESULTS: We developed the decision engine by converting the European Society of Cardiology (ESC) heart failure guidelines into Business Process Model and Notation, a visual modelling language suitable for developing complex decision engines. A safety evaluation, based on clinical parameters, was conducted to ascertain the system's applicability to specific cases. The decision engine renders specific decisions concerning disease- modifying therapy for heart failure patients. We defined 72 virtual heart failure patient scenarios, encompassing a broad spectrum of baseline characteristics and background medication. All recommendations offered by the engine were evaluated by an independent heart failure specialist. All but three recommendations (94%) were identical to the treatment decisions by the heart failure specialist, and all (100%) were in line with the 2021 ESC heart failure guidelines. CONCLUSION: The decision engine offers guideline-based recommendations for disease-modifying therapy, positioning it as a tool to enhance self-care among heart failure patients. To validate our results, the decision engine is being prospectively tested in real-world patients in a multicentre clinical trial (NCT04699253).