This study developed a digital inline holography (DIH) system integrated with deep learning algorithms for real-time detection of particulate matter (PM) and bacterial contamination in peritoneal dialysis (PD) fluids. The system comprises a microfluidic sample delivery module and a DIH imaging module that captures holograms using a pulsed laser and a digital camera with a 40 × objective. Our data processing pipeline enhances holograms, reconstructs images, and employs a YOLOv8n-based deep learning model for particle identification and classification, trained on labeled holograms of generic PD particles, Escherichia coli (E. coli), and Pseudomonas aeruginosa (P. aeruginosa). The system effectively detected and classified generic particles in sterile PD fluids, revealing diverse morphologies predominantly sized 1-5 µm with an average concentration of 61 particles/µL. In PD fluid samples spiked with high concentrations of E. coli and P. aeruginosa, our system achieved high sensitivity (>
90 %) in detecting and classifying these bacteria at clinically relevant low false positive rates (∼0.5 %). Further validation against standard colony-forming unit (CFU) methods using PD fluid spiked with bacterial concentrations from approximately 100 to 10,000 bacteria/mL demonstrated a clear one-to-one correspondence between our measurements and CFU counts. Our DIH system provides a rapid, accurate alternative to traditional culture-based methods for assessing bacterial contamination in PD fluids. By enabling real-time sterility monitoring, it can significantly improve patient outcomes in PD treatment, facilitate point-of-care fluid production, reduce logistical challenges, and be extended to quality control in pharmaceutical production.