When it comes to studying microscopic picture data in cell biology research, robotic processing of information and machine learning approaches are crucial, as discussed in the paper. Because microscope pictures may be so complicated and varied, it shows how difficult it is to develop suitable algorithms for data processing. Tasks like object identification, movement analysis, and morphometric feature extraction are tackled with the help of machine learning methodologies including supervised learning (classification) and unsupervised learning (clustering). We talk about the possible and actual uses of these methods in structural biology and cell biology, as well as the things to think about and the problems to solve when putting them into practice.