In recent years, researchers in statistics and machine learning have begun to concentrate on topological data analysis, or TDA. Clustering and other methods that take use of data's geometry have been very useful in both theory and practice. The persistence homology approach is often used for TDA because it quantifies the importance of these invariants. Use of persistent homology is seen in several data visualization, statistical, and machine learning approaches. When combined with persistent homology and TDA, ML becomes more interpretable and generalizable than with only cutting-edge techniques. A variety of TDA applications and homology of persistence are investigated in my research. Since TDA has already been used to monitor intracellular particle motion, it makes sense that it might also be utilized to analyze the cellular cytoskeleton as a network seen in confocal microscopy pictures.