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Artificial Intelligence (AI) tools based on Machine learning (ML) have demonstrated their potential in modeling climate-related phenomena. However, their application to quantifying greenhouse gas emissions in cities remains under-researched. Here, we introduce a ML-based bottom-up framework to predict hourly CO2 emissions from vehicular traffic at fine spatial resolution (30 × 30 m). Using data-driven algorithms, traffic counts, spatio-temporal features, and meteorological data, our model predicted hourly traffic flow, average speed, and CO2 emissions for passenger cars (PC) and heavy-duty trucks (HDT) at the street scale in Berlin. Even with limited traffic information, the model effectively generalized to new road segments. For PC, the Relative Mean Difference (RMD) was +16% on average. For HDT, RMD was 19% for traffic flow and 2.6% for average speed. We modeled seven years of hourly CO2 emissions from 2015 to 2022 and identified major highways as hotspots for PC emissions, with peak values reaching 1.639 kgCO2 m−2 d−1. We also analyzed the impact of COVID-19 lockdown and individual policy stringency on traffic CO2 emissions. During the lockdown period (March 15 to 1 June 2020), weekend emissions dropped substantially by 25% (−18.3 tCO2 day−1), with stay-at-home requirements, workplace closures, and school closures contributing significantly to this reduction. The continuation of these measures resulted in sustained reductions in traffic flow and CO2 emissions throughout 2020 and 2022. These results highlight the effectiveness of ML models in quantifying vehicle traffic CO2 emissions at a high spatial resolution. Our ML-based bottom-up approach offers a useful tool for urban climate research, especially in areas lacking detailed CO2 emissions data.