This paper proposes a deep learning-based monitoring system for continuous ambulatory peritoneal dialysis called iCAPD. iCAPD comprises a load cell module, a heating and thermal preservation module, a fluid turbidity test chart, a camera module, an Edge-AI computing module, and a display module. iCAPD can heat and preserve the temperature of the dialysate bag at 37°C within 20 minutes. Furthermore, it can detect the dialysate infusion volume by sensing the weight change of the dialysate bag. Moreover, iCAPD uses deep learning-based technology to recognize the turbidity of drainage fluid from the abdominal cavity. The experimental results show that the turbidity recognition performance of iCAPD can achieve the purpose of assisting the assessing the severity of associated peritoneal symptoms (e.g., peritonitis).