Isothermal microcalorimetry (IMC) is a promising tool for diagnosing periprosthetic joint infection (PJI), based on real-time measurement of growth-related heat production of pathogens, and faster than conventional microbial cultures. However, the feasibility of identifying specific pathogens in clinical samples using IMC has yet to be proven. This study implements machine learning and transfer learning convolutional neural network (CNN) models to detect and identify pathogens causing PJI, using IMC data alone. IMC data were obtained from synovial fluid samples, including 174 aseptic samples and 239 PJI samples containing five different bacterial strains. XGBoost, multi-layer perceptron, support vector machine, random forest, and three transfer learning CNN models were implemented to detect PJI and identify five bacterial strains in PJI samples. The binary XGBoost classifier yielded a 100% accuracy in PJI detection, whereas the multiclass XGBoost classifier and the combined transfer learning CNN classifier reached an overall accuracy of 90.3% and 91.5%, respectively, in PJI identification, with biological significance of extracted features in the XGBoost model facilitating its interpretability and usage in clinical practice. The strain with the lowest recall (83.3%) was PA, whereas SE was the strain with the lowest precision (78.9%). The results demonstrate the feasibility of automatic detection and identification of pathogens causing PJI using their IMC growth patterns and machine learning models. This adds a critical missing feature to IMC, contributing to accelerating the diagnosis of PJI and the selection of antibiotic therapy.