Citrullination is an important post-translational modification implicated in many diseases including rheumatoid arthritis (RA), Alzheimer's disease and cancer. Neutrophil and mast cells have different protein-arginine deiminases expression profile and ionomycin induced activation make them the ideal cellular models to study proteins susceptible to citrullination. We performed high resolution mass spectrometry and stringent data filtration to identify citrullination sites in neutrophil and mast cells treated with and without ionomycin. We identified a total of 831 validated citrullination sites on 393 proteins. Several of these citrullinated proteins are important component of pathways involved in innate immune responses. Using this benchmark primary sequence dataset, we developed machine learning models to predict citrullination in neutrophil and mast cells proteins. Our neutrophil protein citrullination prediction model achieved greater than 76% accuracy and 0.39 Matthews correlation coefficient (MCC) on an independent validation set. In summary, this study provides the largest number of validated citrullination sites in neutrophil and mast cell proteins. The use of our novel motif analysis approach to predict citrullination sites will facilitate the discovery of novel protein substrates of protein-arginine deiminases (PADs), which may be key to understanding immunopathologies of various diseases.