The research investigates the sliding motility of Clostridium perfringens by employing machine learning-based image segmentation techniques and tracking to extract key quantitative characteristics of the movement of the bacteria. C. perfringens cells maintain end-to-end connections after cell divisions and form elongated chains that expand in a one-dimensional fashion. Cells in the elongating chains are pushed by each other to achieve a sliding movement at potentially high speeds. However, these chains are susceptible to breakage due to stress accumulation from rapid growth, which would undermine efficiency of the passive sliding motility. Utilizing AI-powered image analysis, this research aims to obtain detailed quantification of these dynamics and generate data for future mechanistic studies of the sliding motility. Results from this work highlight the effectiveness of machine learning in detecting individual cells from microscopy images. The accurately segmented cells enable enhanced tracking and detailed analysis of bacterial motility. The results generate useful quantitative data such as growth rate, velocity, and division frequency of C. perfringens. / Master of Science / The research project explores the movement of Clostridium perfringens, a bacterium often responsible for food poisoning, by using machine learning techniques to observe and analyze how each bacterial cell moves within its colony. These bacteria form long, chain-like structures that help them move more rapidly. However, these chains can break when they become too long and undergo too much stress. By applying artificial intelligence-based tools to automatically detect and track cells in time-lapse microscopy videos, the project provides useful data of how these bacteria slide, grow and divide. These data will help us understand the chain-based bacterial sliding in C. perfringens and its underlying mechanism.