In managing the integrity of bridge structures, damage detection and safety evaluation are crucial. The most important factors that contribute to bridge deterioration are environmental and operational variability. Early monitoring is crucial to maintain and protect the bridge structure over its lifetime. This work therefore intends to use computer vision-based vibration measurement to detect and analyze the vibrations of bridges. The captured data is processed using advanced algorithms to extracted features-based displacement measurement to identify patterns and anomalies in the vibration data and finally assess the bridge condition. This approach can handle the complicated situations by increasing computational efficiency, treating uncertainties, and streamlining the decision-making process. Also, the approach provides useful insights and future trends for applying ML and vibration-based damage detection methods to bridge health monitoring.