With the aging of global population, the prevalence of neurodegenerative diseases (among which Alzheimer Disease, Parkinson’s Disease and Multiple Sclerosis) is rapidly increasing, representing a challenge for the sustainability of the healthcare systems in the forthcoming decades. Machine learning represents a promising tool to build models for the prediction of disease course and the targeting of therapeutic and care strategies. Nevertheless, traditional AI tools require lots of curated data to provide robust and reliable predictions, which might be a challenge when dealing with longitudinal clinical data. Indeed, clinical data are challenging to obtain due to privacy concerns and, whenever available, they might present many missing values, rendering them unsuitable for the training of AI algorithms. The objectives of this research are two fold. The first is to build predictive models of the development of several neurodegenerative diseases to assist clinicians in decision making. The second objective is to investigate methods to mitigate data scarcity challenges. These methods will exploit similarities between these diseases and will include a range of approaches such as transfer learning and generative models. Each of these approaches will then be evaluated in terms of predictive performance in a set of clinically relevant outcomes.