The National Highways Authority of India (NHAI) has introduced an AI-powered dashcam monitoring system covering approximately 40,000 kilometres of the national highway network. The initiative combines artificial intelligence and machine learning to change how operations and maintenance are planned and executed across highways and expressways through a data-driven approach. The authority expects the programme to deliver more timely detection of defects and to prioritise repairs based on objective condition assessments. The project is presented as part of a wider move towards predictive maintenance and improved road safety for users. Specialised dashboard cameras will be mounted on Route Patrol Vehicles to conduct weekly surveys of all designated highway stretches using high-resolution imagery and continuous video capture. Advanced AI and machine learning models will automatically identify over 30 different types of defects and anomalies with particular emphasis on pavement conditions such as potholes, rutting and severe cracking. The monitoring regimen also covers road furniture and signage so that damaged or faded lane markings, compromised crash barriers and non-functional streetlights can be recorded for action. At least one night-time survey will be conducted monthly to assess road signages, pavement markings, road studs and highway lighting performance under operational conditions. The scope of assessment will include additional maintenance concerns such as water stagnation, missing drainage covers, vegetation overgrowth and bus bay conditions that can affect road safety and traffic flow. NHAI has organised the national network into five zones to allow systematic data collection and follow-up. A specialised IT platform with dedicated modules for data management, AI analytics and interactive visualisation dashboards will enable side-by-side comparisons of road conditions across time periods and locations. The arrangement will allow engineers to view trend lines, to score sections for urgency and to plan maintenance interventions more efficiently. AI-generated outputs will be fed into the central NHAI Data Lake platform to ensure seamless monitoring and to trigger timely rectification workflows. The authority anticipates that combining vehicle-borne capture with automated analysis will reduce manual inspection burdens and improve resource allocation.