This paper presents a systematic approach to detecting High Impedance Faults (HIFs) in medium voltage distribution networks using recurrence plots and machine learning. We first simulate 1150 internal faults, including 300 HIFs, 1000 external faults, and 40 normal conditions using the PSCAD/EMTDC software. Key features are extracted from the 3-phase differential currents using wavelet coefficients, which are then converted into recurrence matrices. A multi-stage classification framework is employed, where the first classification stage identifies internal faults, and the second stage distinguishes HIFs from other internal faults. The framework is evaluated using accuracy, precision, recall, and F1 score. Tree-based classifiers, particularly Random Forest and Decision Tree, achieve superior performance, with 99.24% accuracy in the first stage and 98.26% in the second. The results demonstrate the effectiveness of integrating recurrence analysis with machine learning for fault detection in power distribution networks.