Drug dosing in rare diseases is a high-complexity issue where traditional modelling techniques have difficulties in application. This work aimed to design and challenge machine learning algorithms for pharmacokinetic prediction in rare disease populations, with a focus on a deep neural network (DNN). Using a rich dataset including patient demographics and genetic profiles along with drug properties, several models were created and tested with the use of linear regression, random forest, support vector machine, and DNN. The DNN outperformed its counterparts since the mean absolute error (MAE) was 0.140, the root mean squared error (RMSE) was 0.198, and the (R2) value was 0.834, with high predictive accuracy. Pharmacokinetic outcomes were analyzed depending on the main features of a patient's weight and severity of disease using Shapley Additive Planations (SHAP) analysis to make the prediction results more interpretable. Confirmatory tests through validation of the model helped establish the robustness of the model and its applicability to real-world scenarios. The results seem to promise machine learning as a source of revolution for the future of pharmacokinetic predictions in rare diseases, which will suggest personalized dosing strategies that may effectively enhance outcomes in therapy. The study underscores the need to involve advanced data-driven approaches in clinical practice to lead the way for bettering the precision in drug therapy for a rare condition. Future research directions include the integration of additional biological data and real-world clinical testing into such predictive models.