Single-cell flow cytometry is pivotal in biomedical research, offering invaluable insights into cellular phenotypes and functions. However, its potential is often constrained by technical limitations, noise interference, and batch effects. In this context, we propose fcVI, a multimodal deep generative model, tailored for integrative analysis of multiple massively parallel cytometry datasets from diverse sources. By effectively modeling noise variances, technical biases, and batch-specific disparities using probabilistic data representation, we showed fcVI not only excels in missing protein marker imputation but also sets a pioneering standard in seamlessly integrating multiple cytometry panels. As a result, fcVI emerges as a potent tool for constructing comprehensive flow cytometry atlases and enhancing the precision of flow cytometry data analyses.