<p>The dynamic growth in the use of artificial intelligence (AI) in medicine is driven by advances in computer processing power, memory, and data storage capabilities. The digital progress, combined with continuously growing datasets, enables the development of modern tools that support oncological, neurological, and genetic diagnostics. Achievements in the application of AI in medicine make it possible to analyze genotype–phenotype correlations, and rare diseases, which include more than 6,000 distinct conditions characterized by unique phenotypes and complex pathomechanisms, present a particular challenge in this context. This article presents the basic issues related to machine learning and describes the mechanisms of the most commonly used algorithms. In addition, a review was conducted of modern tools that enable the analysis of genotype–phenotype correlations and the prediction of symptoms of the rare disease neurofibromatosis type 1 (NF1). Tools developed so far to support therapeutic decision-making related to NF1 were collected and described. The tools were classified according to their functionality, and their usefulness in the diagnostic process was discussed in detail, emphasizing how advanced algorithms can support precise phenotypic assessment and patient health evaluation and contribute to the optimization of their treatment. Despite its great potential, the use of AI in the diagnosis of rare diseases is associated with challenges such as the need to standardize small patient groups and the necessity of interdisciplinary collaboration between experts in genetics, bioinformatics, laboratory medicine, and clinical medicine.</p>