Age estimation is increasingly vital for regulating access to age-restricted services, especially to protect children online. Traditional methods-ID checks, facial recognition, and databases-raise concerns about privacy and reliability in digital contexts. Electrocardiogram (ECG) signals, reflecting heart activity, offer a promising alternative due to their age-dependent characteristics. However, prior research has largely relied on hospital-grade ECGs, limiting real-world use. To address this, we created a novel data set using smartwatch ECGs from 220 individuals across a broad age range. By testing various features and machine learning models, we achieved a mean absolute error (MAE) of 2.93 years-outperforming clinical ECG-based studies. Accuracy peaked during adolescence, when ECG changes are most pronounced. We also performed binary age classification (13-21 years), reaching 93-96% accuracy. These findings highlight smartwatch ECG's potential for accurate and privacy-respecting age estimation.