We examine the effectiveness of Local Linear Forests (LLF) for forecasting cryptocurrency volatility, comparing their performance against traditional models Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Glosten–Jagannathan–Runkle GARCH (GJR-GARCH), realized volatility approaches Heterogeneous Autoregressive model of Realized Volatility (HAR-RV), and machine learning methods (Random Forests). Using data from eight cryptocurrencies (2021–2024), we find that LLFs consistently outperform benchmark models in out-of-sample forecasting, particularly during market transitions and high-volatility periods. Through extensive simulation studies and empirical analyses, we demonstrate that LLFs exhibit superior adaptability to cryptocurrency markets’ nonlinear dynamics and regime shifts. The economic significance of these improvements is validated through a utility-based framework for risk-targeting investors. Notably, we find that volatility forecasting accuracy is systematically higher for mid-cap versus large-cap cryptocurrencies, suggesting important implications for market efficiency and portfolio management. Our feature importance analysis reveals that simple market metrics are sufficient for effective volatility prediction, challenging the conventional wisdom that cryptocurrency forecasting requires complex high-frequency data inputs. These findings contribute to the growing literature on machine learning applications in financial markets and offer practical insights for cryptocurrency risk management.