In the study of intracellular pathogens like Mycobacterium tuberculosis, time-lapse microscopy is a valuable tool for understanding dynamic cellular processes involved in host cell defense. Quantification of signals at localized compartments within the cell and around bacteria can provide even deeper insight into interactions between bacteria and host cell organelles. However, existing quantitative analysis at a single-bacterial level remains limited and dependent on manual tracking methods. We developed a near-fully automated workflow that performs unbiased, high-throughput cell segmentation and quantitative tracking of both single cells and single bacteria/phagosomes within multi-channel, z-stack, time-lapse confocal microscopy videos. We took advantage of the PyImageJ library to bring Fiji functionality into a Python environment and combined deep-learning-based segmentation from Cellpose with tracking algorithms from Trackmate and visualization within ImageJ. Following both cell and bacteria tracking, our workflow provides a versatile toolkit of functions for measuring relevant signal parameters at the single-cell level (such as velocity or bacterial burden) and at the single-bacteria level (for assessment of phagosome maturation). Ultimately, our workflow’s capabilities in both single-cell and single-bacteria quantification can help decipher the virulence factors of pathogens and pave the way for the development of innovative therapeutic approaches. The customizable nature of the methods extends the applications of the workflow far beyond the field of mycobacteria and presents opportunities for advancement in host-pathogen interaction research in a variety of systems.