Bridge management systems (BMSs) are essential for managing the lifecycle of bridges, ensuring safety, and optimizing maintenance. This review discusses current practices in BMSs, emphasizing their role in a digitizing world. Bridges face various deterioration issues, including corrosion, fatigue, and damage from events like earthquakes, which challenge their performance and safety. Effective BMSs help manage these challenges by optimizing budget allocation and intervention strategies. Existing studies on BMSs focus on specific aspects, lacking a comprehensive overview of the entire process. This review introduces a new definition of BMS modules: data management, diagnosis, prognosis, and decision-making. It covers the historical and current practices of common BMSs, outlining their principles, critical aspects, and future trends. BMSs have evolved over decades, with early examples like the Danish DANBRO BMS and the Netherlands' DISK BMS. Today, numerous countries have developed their own BMSs, including Italy's APTBMS, the US's Pontis, and Canada's OBMS. These systems vary in design and functionality, often incorporating digital tools like Bridge Information Modelling (BrIM) and Digital Twins. Despite advancements, there is a lack of consensus on standardized BMS modules. This review proposes a unified definition for these modules, emphasizing their interconnectedness. Data management in BMSs involves collecting, acquiring, transmitting, and storing data from inspections and structural health monitoring (SHM). Inspections, including visual, non-destructive testing (NDT), and destructive testing (DT), provide critical data on bridge conditions. SHM systems use sensors and data acquisition devices to monitor structural health continuously. These systems face challenges, including the need for continuous maintenance and the difficulty in distinguishing between anomalous data and actual damage. Diagnosis and prognosis modules assess bridge conditions using performance indicators (PIs), which include technical and non-technical metrics. Technical PIs evaluate structural properties and degradation, while non-technical PIs consider economic, social, and environmental factors. Condition assessment based on BCIs (Bridge Condition Indicators) helps prioritize maintenance and repair actions. SHM data is used to extract damage indicators (DIs), which help identify and quantify damage. Machine learning and data-driven approaches enhance the accuracy of these assessments. This review highlights the importance of standardized BMS modules and the integration of digital technologies in bridge management. It underscores the need for continuous research and development to improve BMSs, ensuring the safety and efficiency of bridge infrastructure in a rapidly digitizing world.Bridge management systems (BMSs) are essential for managing the lifecycle of bridges, ensuring safety, and optimizing maintenance. This review discusses current practices in BMSs, emphasizing their role in a digitizing world. Bridges face various deterioration issues, including corrosion, fatigue, and damage from events like earthquakes, which challenge their performance and safety. Effective BMSs help manage these challenges by optimizing budget allocation and intervention strategies. Existing studies on BMSs focus on specific aspects, lacking a comprehensive overview of the entire process. This review introduces a new definition of BMS modules: data management, diagnosis, prognosis, and decision-making. It covers the historical and current practices of common BMSs, outlining their principles, critical aspects, and future trends. BMSs have evolved over decades, with early examples like the Danish DANBRO BMS and the Netherlands' DISK BMS. Today, numerous countries have developed their own BMSs, including Italy's APTBMS, the US's Pontis, and Canada's OBMS. These systems vary in design and functionality, often incorporating digital tools like Bridge Information Modelling (BrIM) and Digital Twins. Despite advancements, there is a lack of consensus on standardized BMS modules. This review proposes a unified definition for these modules, emphasizing their interconnectedness. Data management in BMSs involves collecting, acquiring, transmitting, and storing data from inspections and structural health monitoring (SHM). Inspections, including visual, non-destructive testing (NDT), and destructive testing (DT), provide critical data on bridge conditions. SHM systems use sensors and data acquisition devices to monitor structural health continuously. These systems face challenges, including the need for continuous maintenance and the difficulty in distinguishing between anomalous data and actual damage. Diagnosis and prognosis modules assess bridge conditions using performance indicators (PIs), which include technical and non-technical metrics. Technical PIs evaluate structural properties and degradation, while non-technical PIs consider economic, social, and environmental factors. Condition assessment based on BCIs (Bridge Condition Indicators) helps prioritize maintenance and repair actions. SHM data is used to extract damage indicators (DIs), which help identify and quantify damage. Machine learning and data-driven approaches enhance the accuracy of these assessments. This review highlights the importance of standardized BMS modules and the integration of digital technologies in bridge management. It underscores the need for continuous research and development to improve BMSs, ensuring the safety and efficiency of bridge infrastructure in a rapidly digitizing world.