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Potholes Are A Major Cause Of Road Accidents, Vehicle Damage, And Traffic Disruptions, Making Their Timely Detection And Repair A Critical Aspect Of Modern Transportation Management. Traditional Manual Inspection Methods Are Labor-intensive, Time-consuming, And Prone To Human Error, Highlighting The Need For Automated, Reliable, And Scalable Solutions. Recent Advancements In Computer Vision And Deep Learning Have Opened New Opportunities For Accurate Pothole Detection And Measurement. This Study Proposes A Modern Pothole Detection And Dimension Estimation System That Integrates The YOLO (You Only Look Once) Deep Learning Model With Advanced Image Processing Techniques. The YOLO Architecture Is Employed For Real-time Pothole Localization And Classification, While Image Processing Methods Are Utilized To Estimate The Dimensions Of Detected Potholes, Such As Width, Depth, And Surface Area. The Proposed System Ensures High Accuracy, Robustness Under Varying Lighting And Environmental Conditions, And The Ability To Process Large-scale Road Datasets Efficiently. Experimental Results Demonstrate That This Hybrid Approach Outperforms Traditional Methods, Offering A Practical And Cost-effective Solution For Smart City Infrastructure, Automated Road Condition Monitoring, And Predictive Maintenance Systems.