Learning disabilities present significant barriers in the lives of individuals, particularly children and students, as they can impede their learning process and skill development. Dysgraphia, a form of learning disability, can adversely affect an individual's writing ability. While various approaches have been proposed to detect learning disorders, there is a lack of methods for tracking the progression of these disorders. In this work, we propose an intelligent model for tracking the evolution of dysgraphia. Our approach utilizes a probabilistic machine learning algorithm to compute a Dysgraphic class score for each individual. By computing this score at different intervals, we can monitor the individual's progress over time. To achieve this, we trained various probabilistic classifiers and fuzzy clustering algorithms on a labeled dataset to select the model with the best performance for tracking. Our experimental evaluation demonstrates that our model successfully tracks the evolution of individuals with dysgraphia.