Nigeria is one of the countries with the highest incidence of Hepatitis B Virus (HBV) infection worldwide. The inaccessibility and unaffordability of HBV DNA quantification (viral load) assay which is the key laboratory test for therapy initiation, and monitoring is a challenge to HBV management. This study aimed at evaluating the HBV DNA viral load differences across the serological markers of HBV in order to develop a more cost-effective diagnostic algorithm for Hepatitis B management. Cross sectional study design was used with a total of 264 subjects comprising of 88 HBsAg seropositive treatment naïve subjects, 88 HBsAg seropositive subjects on antiviral therapy as case subjects and 88 age-matched apparently healthy HBsAg seronegative individuals were recruited as control subjects. Hepatitis B Virus DNA assay was performed using real time PCR technique, Hepatitis B core Antibody Immunoglobulin M and Hepatitis D Virus Immunoglobulin G assay. Immunochromatography was used for HBV Panel, Hepatitis C Virus assay and Human Immunodeficiency Virus testing. The non-treatment group has higher viral load (M=805.50 IU/ml) compared with treatment group (M = 65.50 IU/ml) (p<0.001). There was a significant difference in the HBV DNA levels of the four serological patterns observed in the study (P< 0.001). Among the four serological patterns observed, the second pattern with positive surface antibody, positive envelope antigen and negative envelope antibody showed highest viral load (M= 46850189.50 IU/ml) followed by the third pattern with negative surface antibody, negative envelope antigen, and negative envelope antibody (M=46555 IU/ml). The first pattern which has negative surface antibody, negative envelope antigen and positive envelope antibody has the lowest viral load (M=21.00 IU/ml) followed by the fourth pattern with positive surface antibody, negative envelope antigen and positive envelope antibody (M=493.00 IU/ml). This study showed that HBV serological markers can predict viral load and should be used as its alternative in resource poor settings.