The primary duty of law enforcement agencies is to ensure that a victim has the necessary information and access to the relevant tools required to seek justice. In India, complex cases such as bodily offences and property crimes capture the work and efforts of many agencies involved; however, cases related to missing persons are not often accorded similar priority or seriousness. The COVID-19 pandemic and subsequent lockdowns have added further challenges to this scenario. The government-mandated lockdowns in Tamil Nadu generally exacerbated difficult socio-economic and living conditions, thereby directly or indirectly contributing to an increased load of missing person cases. This study aims to assess and identify the impact of mobility on reporting and registration of missing persons. By adopting an auto-regressive neural networks method, this study uses a counterfactual analysis of registered missing person cases during the government-mandated lockdowns in response to the global pandemic in 2020 and 2021. The registered cases are calculated based on the daily count of cases for eleven years in Tamil Nadu, India. The lockdowns identify eight different time windows to determine the impact of mobility on the registration of cases. While there has been no significant or drastic change over the pre-pandemic period, during the pandemic, especially during the restrictive phases of the pandemic, there was a sharp fall in cases compared to the counterfactual predicted (effect sizes: −0.981 and −0.74 in 2020 and 2021), signalling towards a choked mechanism of reporting. In contrast, when most mobility restrictions were removed, an increase in cases (effect sizes of +0.931 and 0.834 in 2020 and 2021) pointed to restored and enabled reporting channels. The research findings emphasise the significance of mobility as a factor in influencing the reporting and registration of missing persons and the need to ensure this continues to help families find redress.