The advancement of technology has led to an increase in experimentations with data centric design models to generate various forms inconceivable by the human mind. However, their application is constrained to building performance, optimization, and the functional aspects of the design problem only. However, qualitative elements, like social and contextual aspects which are significant in tackling architectural issues are normally ignored. The study aims to recast computational tools like machine learning and pattern recognition as crucial design agents for urban housing. The study investigates the British Indian Architect Charles Correa’s Housing projects to understand the underlying qualitative aspects behind them using machine learning and also uses this as a universal approach to examine any architecture.