Background: Internal migrant children in India constitute one of the most invisible populations. They fall outside school enrolment records and health registers, as families follow labour migration cycles. No longitudinal community surveillance model for this population exists in the literature. Photovoice, a participatory action research methodology used to document lived experience through photography , has been applied at a small scale in some populations but never as a digital surveillance method. Objectives: The WhatsApp-documented Photovoice surveillance model for internal migrant children across eight cities in India and to assess its potential as a replicable low-resource framework for child welfare monitoring. Methods: I Light Global, an NGO with four years of continuous field operations, established a network of volunteer-led field centres across eight Indian cities. Each centre has a woman volunteer who opens her home to volunteers and children on a daily basis. Following each session, three to ten photographs documenting educational activity , nutritional provision, and support are shared in a single common WhatsApp group accessible to directors, volunteers, and teachers. Results: The network has generated numerous photographs across eight cities, representing the largest communitydocumented longitudinal visual welfare record of internal migrant children. Welfare domains consistently documented photographs on education, nutritional provision, material support, cultural participation, and recreation. The WhatsApp platform functions simultaneously as real-time governance infrastructure also. Conclusions: This programme represents the first documented application of Photovoice methodology at a multi-city scale using WhatsApp as the longitudinal surveillanceinfrastructure with internal migrant children. The model is low cost and replicable, requiring no formal training.