User data is collected during empathize phase of design thinking to get useful insights from the user perspective before embarking on the design process. User data may be collected from interviews, surveys, and brainstorming sessions. This user data can be used to identify user pain points. User pain points are the user dissatisfaction with a service or a product. Manual processing of data to identify pain points is time consuming and susceptible to mistakes. Recently, machine learning (ML) algorithms are being employed to automate the detection of pain points. In this chapter, we propose the application of ML algorithm namely Latent Dirichlet Allocation (LDA) to identify the user pain points from data collected during the design thinking process. LDA is a generative probabilistic model widely used across various domains to infer valuable insights from unstructured data. We demonstrate the algorithmic detection of pain points using machine learning through the design of a Web application for a student record management system using design thinking. The pain points serve as a basis for identifying user requirements of the system.