Background and Objectives: Understanding the structural differences in the sacroiliac joint (SIJ) is essential for distinguishing inflammatory from degenerative disorders. This study aimed to evaluate disease-related morphological patterns and morphometric characteristics of the sacroiliac joint. Additionally, machine learning models were applied to classify inflammatory, degenerative, and control groups based on the morphological and morphometric characteristics of the sacroiliac joint. Materials and Methods: This retrospective study included Magnetic Resonance Imaging (MRI) images of 209 individuals (a total of 418 sacroiliac joints) between the ages of 18 and 75. Participants’ age, sex, disease-related sacroiliac joint morphological features (joint surface type), erosion, sclerosis and inflammation in the joint were determined. Right/left joint space and right/left joint length were measured. According to these anatomical features, machine learning models and a deep neural network were used to classify joints as control, inflammatory, or degenerative. Stratified 5-fold cross-validation was used. Results were reported as mean ± SD with macro averaged precision, recall, and F1-score. Results: The degenerative group was significantly higher than the other groups in terms of mean age (p = 0.001). Both right and left sacroiliac joint spaces were significantly narrower in the inflammatory and degenerative groups than in controls (right SIJ space: p = 0.002; left SIJ space: p = 0.001). Erosion was significantly more frequent in pathological groups (p = 0.001). Although the iliosacral complex was the most common joint type in all groups, no significant difference was observed between the disease groups (right, p = 0.852; left, p = 0.935). In classification, SVM (RBF) and XGBoost achieved the highest accuracy (both: 0.9518 ± 0.0380 and 0.9518 ± 0.0436, respectively) and macro-F1 (0.9509 ± 0.0387 and 0.9506 ± 0.0443). Conclusions: Disease-related morphological and morphometric changes in the sacroiliac joint can be reliably assessed with MRI. These features can then be used in machine learning models to differentiate between inflammatory and degenerative joint disorders. Carefully examining these anatomical features plays a key role in reaching an accurate diagnosis. Machine learning supports this process by helping to interpret the findings in a more consistent and objective way.