<p>The study aims to provide an insight into scientific trend of publications, collaborative efforts, and impression of research on the key applications of Unmanned aerial vehicles (UAV) using deep learning for human safety. The study presents an integrated approach of bibliometric, interpretive structural modelling (ISM) and MICMAC analysis to explore the breadth and depth of research area in one hand and to determine the interrelationships between key applications of UAV for timely rescue of human lives on other. In the initial stage using the keywords "Deep Learning" AND "Unmanned Aerial Vehicle" OR "UAV" AND "Human safety" within the timeframe 2017 to 2023, 203 documents were extracted from Web of Science (n = 201) and Scopus (n = 2) database. After data filtration, bibliometric analysis is carried out using 108 documents that briefs on prominent research trends of publication, citation, affiliations, authors and countries along with h-index, g-index and m-index of actively contributing journals. Further, the analysis made use of ISM and MICMAC analysis through a combination of 45 academic and industry expert interviews and extensive literature review that finalized 9 validated key applications which are further segregated into three-levels and four-quadrants based on their dependence and driving power. This study can possibly be beneficial for academic researchers and industry experts as the analysis synthesizes a broad range of published work in finding out the key applications of UAV in human safety such that the gaps can be overcome by assembling evidence from literature and integrating the findings for a clear understanding of the matter.</p>