This peer-reviewed narrative review evaluates the role of artificial intelligence (AI), robotics, and navigation-assisted technologies in the care of osteoporotic vertebral compression fractures (OVCFs). The clinical question is whether these tools can improve diagnostic accuracy, guide safer treatment, and predict outcomes in a condition with rising global incidence. Evidence is drawn from retrospective and prospective studies across imaging, surgical navigation, robotics, and machine learning prognostic models. In diagnosis, convolutional neural networks (CNNs) improved fracture detection on X-rays (accuracy ~89%) and CT (sensitivity 85%, specificity 92%, accuracy 89%), outperforming radiologists in some settings. MRI-based CNNs differentiated osteoporotic from malignant fractures with accuracies >93%. In treatment, C-arm and O-arm navigation, 3D-printed surgical guides, mixed reality, and robot-assisted kyphoplasty increased puncture precision, reduced radiation, and lowered cement leakage. In prognosis, machine learning predicted refractures (Random Forest AUC 0.94), residual pain (SVM accuracy 91%), and cement leakage (RF AUC 0.90) more accurately than regression models. Some of the limitations are the heterogeneous datasets, lack of external validation, and missing subgroup analyses.