This paper investigates the impact of traffic congestion on fuel consumption and CO₂ emissions in hybrid electric vehicles (HEVs) operating in urban driving conditions. The analysis encompassed four distinct HEV configurations—PHEV, mixed, MHEV, and split-axle hybrid—and evaluated their performance across five urban routes (T1–T5) exhibiting diverse congestion levels, as well as under the WLTC Low cycle. The data were acquired via simulation using AVL Cruise M, leveraging velocity profiles recorded during peak afternoon traffic. The Monte Carlo method was employed to estimate result variability, yielding 24,000 data points per variable. Findings reveal that PHEVs exhibited the most favorable performance, achieving the lowest fuel consumption (0.00–0.02 l) and CO₂ emissions (0.00–0.05 kg), particularly in stop-and-go urban driving due to their electric operating mode. MHEVs demonstrated the least efficiency, with mixed and split-axle hybrid vehicles performing at an intermediate level. ANOVA and Tukey's post-hoc test statistically validated significant distinctions across HEV types and routes. This research underscores the criticality of real-world testing environments and offers valuable implications for HEV design and the evolution of testing protocols for sustainable urban transportation.