Neurological disorders are a major threat to the wellness of individuals. The neurological disorders included in the Global Burden of Disease (GBD) Study—Alzheimer’s and other dementias, Parkinson’s disease, multiple sclerosis, epilepsy, and headache ailments represent 3 percent of the global burden of illness. Though there are very many diagnostics approaches available for early diagnosis of neurological ailments, systems biology approaches, on the other hand, would attempt to identify the systems which, when altered, change the entire body by a ‘Healthy’ into a ‘disease’ state. In the present study, retrieval of selective genes that are related to Neurological Disorders (Epilepsy, Autism, Migraine, Alzheimer’s and Parkinson’s disease) was done by intense data mining using NCBI (https://www.ncbi.nlm.nih.gov/). The PPI (Protein-Protein Interaction) network was constructed using these genes by the use of STRING 11.0 database. The experimental and co-expression data with 0.400 confidence score were taken as key parameter for PPI network construction in STRING. Further the constructed network was subjected to network analysis and visualization using Cytoscape v 3.8 plug-in Network analyzer. Based on topology parameter betweenness centrality (BC) and node degree INS, AKT1, ALB, IL6 and TP53 genes are identified as the key genes in network. INS gene was obtained as a super hub gene amongst all other genes having the highest betweenness centrality (BC) of 0.0598 and node value of 293. The enrichment analysis of INS, AKT1, ALB, IL6 and TP53 reveals their active role in regulation of pathways and processes which are related to the selected neurological disorder. Thus, the study on these genes along with their pathways and biological mechanism can provide a potential target that may lead to the discovery of potential biomarkers for early detection, diagnosis and monitoring of neurological disorders at different stages. Keywords: Neurological Diseases, Systems Biology, Network Analysis, Neurodegeneration.