Cold-formed steel (CFS) shear walls with steel sheathing have gained widespread adoption in multi-story buildings owing to their favorable seismic performance. However, existing design approaches such as the effective strip method (ESM), tabulated methods, and numerical simulations suffer from inherent trade-offs among prediction accuracy, computational cost, and applicability. To address these limitations, this paper develops a machine learning (ML) framework for reliably predicting the shear strength of CFS shear walls with steel sheathing. A hybrid database combining 230 experimental and 100 validated finite element (FE) results was developed, covering key geometric, material, and detailing parameters. Six ML models were trained and evaluated against current methods in AISI S400. Results indicate that ensemble models, especially eXtreme Gradient Boosting (XGB) and Random Forest (RF), achieve superior predictive accuracy and robustness compared to linear models. Notably, the proposed XGB model consistently outperforms existing codified methods across U.S., Canadian, and other regional datasets, while successfully quantifying the contribution of the blocking to shear strength. Reliability analysis further yields a resistance factor of 0.82, exceeding the code-specified seismic design value, thereby confirming the model’s potential to support more efficient and reliable shear wall design.