Meningioma is the most common benign intracranial tumour that develops in the meningeal protective covering of the central nervous system (CNS). Globally, every nine individuals out of 100,000 are diagnosed with this cancer. Basic risk factors of meningioma comprise ionizing radiation, hormonal imbalance, and genetic aberrations. In this study, various bioinformatics tools, specialized for consensus-based identification, sequence-homology, and supervised learning, were employed to analyze and screen the deleterious mutational landscape of commonly associated genes of meningioma/genes commonly associated with meningioma. This study employed an in-silico approach aimed to utilize thirteen different tools to benchmark pathogenic single nucleotide polymorphisms (SNPs) in SMARCB1, AKT1, SMO, SUFU, NF2 and MTHFR genes related to meningioma. We identified six highly pathogenic SNPs related to meningioma: SMARCB1 (rs387906812, rs387906811, rs267607072), AKT1 (rs121434592), SMO (rs121918347), and SUFU (rs202247756). Additionally, several deleterious missense variants of NF2 and MTHFR genes were also identified. Hence, this study is a gateway for research on SNPs since they can be utilized to conduct a type-based diagnosis of meningioma for its early prognosis. They can also be utilized as genomic targets for a targeted therapy by developing inhibitors against mutated proteins. For this purpose, further wet-lab experiments and genome-wide association studies are required to genotype these SNPs in a large number of samples, collected from different populations belonging to various ethnicities, for the development of SNP(s) gene panels.