INTRODUCTION: There are considerable gaps in the conventional pharmacovigilance (PV) measures which might result in significant safety issues, especially in monitoring the effectiveness of orphan drugs that are used to treat rare diseases. In this paper, we evaluate if and how Artificial Intelligence (AI) and Machine Learning (ML) can be used to mitigate these problems. AREAS COVERED: The article identifies ineffective adverse events (AE) reporting systems, low patient enrollment, and weak signal monitoring as barriers to the effective safety evaluation of rare diseases. It also addresses the possibility of employing AI and ML technologies to automate the reporting of AEs by integrating data from multiple sources and increasing the sensitivity of risk detection. The method to conduct the literature search consisted of searching Pubmed and Google Scholar for relevant AI and ML studies and publications aboqut PV. EXPERT OPINION: We identified technical and regulatory concerns such as privacy and model explainability as hurdles to the adoption of AI in PV. However, the same technology, if properly integrated into the system, has the potential to enhance treatment monitoring for rare diseases and to increase the rate of new therapies being developed.