Abstract Background Adolescent idiopathic scoliosis (AIS) is a prevalent musculoskeletal condition affecting approximately 2–3% of the adolescent population. Although exercise-based therapeutic interventions are increasingly employed as non-surgical alternatives, their clinical and economic effectiveness remains underexplored in real-world settings. Recent advancements in active learning (AL) and machine learning (ML) techniques offer the potential to optimize treatment protocols by uncovering hidden predictors and enhancing model efficiency. Methods This retrospective study evaluated the clinical and cost-effectiveness of exercise-based therapy in 128 AIS patients treated between 2020 and 2023 at a tertiary public hospital. Patients were followed for 3 to 36 months. Clinical outcomes were assessed based on changes in Cobb angle, Visual Analogue Scale (VAS) scores for pain, and SRS-22r functional outcomes. Direct medical costs were extracted from institutional records to estimate the incremental cost-effectiveness ratio (ICER) and quality-adjusted life years (QALYs). In parallel, ML models, including Random Forest regression and AL strategies, were applied to predict treatment outcomes and enhance data labeling efficiency. Results Exercise-based therapy resulted in a mean Cobb angle reduction of 6.8° (SD = 3.1), with significant improvements in pain and function (p < 0.001). The ICER was estimated at $1,730 per additional degree of Cobb angle correction, with a projected QALY gain of 0.03 per patient. While treatment duration was statistically non-significant in traditional regression analyses (p > 0.1), ML models identified it as a top predictor of both Cobb angle correction and pain reduction. The Random Forest model achieved an MAE of 0.84 and an RMSE of 1.06 for pain reduction predictions, while AL improved classification accuracy from 65 to 85% across five iterations by selectively labeling the most uncertain cases. Sensitivity analyses confirmed the robustness of economic findings. Conclusion Exercise-based therapy, combined with ML and AL techniques, appears to be a clinically effective and economically sustainable intervention for AIS management. ML models identified important predictors overlooked by classical methods, particularly highlighting the importance of treatment duration. These findings may inform evidence-based strategies for integrating personalized, data-driven approaches into conservative scoliosis treatment protocols and optimizing musculoskeletal healthcare resource allocation.