Genomic medicine stands to be revolutionized through the understanding of single nucleotide variants (SNVs) and their expression in single-gene disorders (mendelian diseases). Computational tools can play a vital role in the exploration of such variations and their pathogenicity. Consequently, we developed the ensemble prediction tool AllelePred to identify deleterious SNVs and disease causative genes. In comparison to other tools, our classifier achieves higher accuracy, precision, F1 score, and coverage for different types of coding variants. Furthermore, this research analyzes and structures 168,945 broad spectrum genetic variants from the genomes of the Saudi population to denote the accuracy of the model. When compared, AllelePred was able to structure the unlabeled Saudi genetic variants of the dataset to mimic the data characteristics of the known labeled data. On this basis, we accumulated a list of highly probable deleterious variants that we recommend for further experimental validation prior to medical diagnostic usage.