Metro Manila's severe traffic congestion and air pollu-tion demand accurate emission assessment tools. Standard driving cycles often fail to capture unique local conditions, leading to flawed inventories. This paper details the data-driven development and valida-tion of the Metro Manila Driving Cycle (MMDC). Re-al-world vehicle data (GPS, OBD-II) were processed to construct the MMDC via micro-trip clustering and synthesis. The MMDC features significantly lower average speed (~20.3 km/h) and higher idle time (~29%) than standard cycles (FTP-75, WLTC Class 3b). Validation against field data confirmed its representa-tiveness, with kinematic parameters and speed-acceleration probability distributions showing strong correlation. The MMDC enables more accurate vehicle emission estimations and informs targeted emission reduction strategies for Metro Manila.