This case study discusses spontaneous resorption in lumbar disc herniation (LDH) as a non-surgical treatment pathway, highlighting the role of machine learning. A 40-year-old female with an L5-S1 herniation underwent two years of conservative treatment including NSAIDs, heat and massage therapy, acupuncture, and kinesitherapy. Ultimately, she experienced significant improvement, demonstrated by a reduced VAS pain score and MRI evidence of complete spontaneous resorption of the herniated disc. Through a systematic review of existing literature, the researchers found that spontaneous resorption is driven by macrophage infiltration, inflammatory responses, neovascularization, matrix degradation, disruption of immune privilege, apoptosis, autophagy, and disc dehydration. Additionally, a number of predictors associated with resorption were identified. Imaging predictors included larger herniations, extrusion or sequestration-type LDH, a higher proportion of nucleus pulposus, posterior longitudinal ligament (PLL) penetration, increased rim enhancement thickness, and Modic changes on contrast-enhanced MRI. Clinical predictors included shorter symptom duration, milder pain, and less severe neurological deficits. Enhancing machine learning and deep learning models through continued research can improve their use as predictors of resorption. Improving data quality and standardization, expanding sample sizes and generalizability, and optimizing algorithms can increase prediction accuracy of artificial intelligence (AI) models. With accurate and reliable models, AI could support early identification of patients most likely to benefit from a conservative treatment plan.