Urban traffic congestion is a growing problem in cities worldwide, leading to significant economic losses, environmental impacts, and commuter frustration. This study investigates traffic flow dynamics at Palanpur Aroma Circle, a critical intersection in Palanpur, Gujarat, India, experiencing severe congestion. We employ data-driven microscopic simulation using SUMO (Simulation of Urban Mobility) to analyze the relationships between traffic volume, road geometry, and speed, and their combined effects on congestion. Real-world traffic data for volume, speed, and road geometry is collected and integrated into the SUMO model. The calibrated model is then used to assess traffic flow efficiency under various scenarios, including baseline conditions and those with modified traffic volumes, road geometry changes, and speed limit adjustments. Statistical analysis and scenario comparisons are conducted to identify the root causes of congestion and evaluate the effectiveness of potential mitigation strategies. This study aims to provide valuable insights for developing data-driven solutions to improve traffic flow and alleviate congestion at Palanpur Aroma Circle and similar intersections in urban areas.