Golden Quadrilateral route accounts for more than 70% of traffic on the Indian railway, so it’s important to use this network efficiently. In this project, we are considering two routes of a Quadrilateral network viz MMCT-NDLS and MAS-NDLS. We used class-based implementation with Train class having different attributes such as arrival time, departure time, station visited, etc. K Means Clustering algorithm was chosen to cluster train into groups with similar property. We compared this cluster to find a maximum dailyzing schedule. Dialyzing term will often be used in this report which means to convert a nondaily train running in a certain path to a daily or near-daily train with similar coverage in terms of the path covered. Clustering results produced can help the Indian railway to make the right decision on adding or removing certain trains in a route. This decision can help them to optimize their profit and people can also get an efficient service with less overall delay