An X-linked genetic disorder, Hemophilia A, occurs due to mutations in the F8 gene and impairs hemostasis by producing coagulation Factor VIII in insufficient quantities or at dysfunctional levels. It is only through comprehension of these mutations and protein alterations that one may increase understanding of Hemophilia A genetic underpinnings and the precision of diagnosis. This work introduces a novel approach called Hemophilia A Severity Prediction (HASP), which classifies the severity of the disease by integrating advanced computational methodology with a feature-rich dataset. The dataset used in this study was obtained from the EAHAD Coagulation Variant Databases and consists of data from 7,782 Hemophilia A patients. It has been treated through several stages, comprising data cleaning, sophisticated feature engineering, and encoding methodologies such as label encoding and position-specific mutation. The HASP model attains an accuracy of 85.76% on this dataset, which is an 11.8% improvement over the baseline study for hemophilia A, validated using Friedman’s test. By employing gene analysis and machine learning, this study improves the disease’s predictive power and provides a solid framework that offers important insights into experimental approaches for predicting the severity of other genetic disorders. The approach created in this work is a breakthrough in precision medicine and offers a fresh perspective on the analysis of genetic disorders. Furthermore, it can assist in the early detection of severe instances and help physicians decide which patients are eligible for gene therapy depending on severity levels.