We examine the role of the technology in predicting carbon prices using a large set of machine learning models and predictors selected from technological, environmental, financial, energy and geopolitical aspects. Our sample covers the daily period from August 1, 2014, to March 4, 2024. We find that technology factors (Information Technology index, AEX Technology index, Tech All Share index), significantly improve the prediction accuracy of carbon prices when included in the prediction model individually and simultaneously. Furthermore, the Diebold-Mariano and Clark-West tests highly reject the null of equal predictive accuracy between the technology model and the baseline model (without technology variables). Moreover, results show that XGBoost outperforms the alternative machine learning models for all forecasting horizons (1 day, 5 days, 22 days, 250 days). We present significant policy implication useful for investors, companies and policymakers.