<p>This study employs a network-based approach to explore the mutation landscape associated with haemophilia ‘A,’ a genetic blood disorder characterized by alterations in the genome. This research adopts a network-centric perspective to analyze the mutation patterns underlying Haemophilia ‘A’ disease. To accomplish this, we utilize the SNP based Omics Networks approach, which necessitates creating networks encompassing SNP mutation at genomic and proteomic level. These networks represent mutations in amino acids and codons, respectively—we analysed the frequency and patterns of the mutations in networks. The methods for the&#xa0;analysis of SNP networks include various centrality measures, community structure detection, assortativity evaluations, and resilience assessments. The results identified that Arginine (<i>Arg</i>) is a critical component in mutation pathways. Community analytics indicated that specific mutations tend to occur concurrently, suggesting interaction networks within the genomic sequences. Additionally, resilience analysis revealed the network’s vulnerabilities, highlighting potential therapeutic intervention targets. By using extensive pyvis network library, highly interactive SNP networks created on the basis of degree centrality. This study not only enhances our understanding of the genetic architecture of haemophilia but also demonstrates the utility of network analysis in unveiling the complexities and identifying the critical omics data of genetic diseases.&#xa0;Identifying critical pathways paves the way for selecting optimal profiles to identify candidates ideally suited for advanced treatment alternatives.</p>