Obesity is a complex metabolic disorder with multiple contributing factors, including genetic, environmental, and lifestyle influences. Recent studies suggest that microbial infections, including viral infections, contribute to obesity. One such infection, Human Adenovirus 36 (HAdV-D36), has been implicated in the development of obesity by affecting metabolic processes, gene expression, and potentially even epigenetic modifications that alter adipose tissue function. While research shows that HAdV-D36 infection can increase adiposity in various animal models, and seroprevalence of the virus is correlated with obesity in humans, identifying individuals naturally infected with the virus has been challenging due to viral sequestration and heterogeneous distribution in adipose tissue. To address this limitation, we propose the development of an indirect enzyme-linked immunosorbent assay (ELISA) to detect HAdV-D36-specific antibodies rather than viral particles. Using bioinformatics analysis, we identified the fiber knob protein of HAdV-D36 as a strong candidate for antibody detection due to its serotype-specific variability and immunogenicity across the virus's 56 serotypes. This protein is not only surface-exposed and capable of eliciting an immune response but also variable and serotype specific, making it an ideal target for serological detection. Our approach, informed by sequence alignments and B-cell epitope prediction, provides a robust and reliable method for detecting HAdV-D36 infection in humans. This new diagnostic tool could significantly advance our understanding of the role HAdV-D36 plays in obesity, helping to establish a clearer link between the virus and metabolic dysfunction. Furthermore, it opens the door for potential preventive measures, such as targeted vaccines, to reduce the impact of this infection on obesity prevalence.