The blend of clinical qualitative and quantitative approaches is used in the early diagnostic process. With the advancement of health monitoring technology and the availability of fitness bands and applications, the paper put forward the approach to use gait monitoring for recognizing the pervasiveness of neurological disorders. The gait changes can be considered as primary indicators in these cases. Prevalence of Cerebral palsy, Alzheimer’s, Parkinson’s, etc. reflects the changes in gait alterations. The model proposes the inclusion of gait analysis and monitoring as one of the approaches to identify neurological disorders and lifestyle disease incidences. The key gait monitoring features are decided on the basis of statistical analysis and machine learning algorithms like PCA, SVM, and DT are tested for performing gender classification. Based on the classification results the prominent features in male and female categories and features to be considered in different age groups are derived for further analysis. Classification accuracy up to 79% is achieved with step length, stride length, and cadence parameters in barefoot walking and it remains at 75% for shoe wear.