This mixed methods longitudinal study aimed to comprehensively investigate changing malaria prevalence patterns in relation to socioeconomic determinants within Mitooma District, Uganda between January 2020 and December 2022. A sequential exploratory research design was employed incorporating retrospective quantitative analysis of routinely collected malaria case surveillance records from all 22 public health facilities during the 3-year period to examine fluctuations and distribution of diagnosed cases according to demographic characteristics, locality, poverty levels and other exposure variables. Concurrently, qualitative interviews and focus group discussions were conducted with purposefully selected community caregivers and frontline workers to provide explanatory contextual insights into transmission dynamics from grassroots perspectives. SPSS software facilitated statistical analyses of surveillance trends and associations while NVivo aided qualitative thematic synthesis. Robust triangulation of obtained quantitative and qualitative findings facilitated a well-rounded evidence-based situational understanding with targeted recommendations for accelerating locally tailored malaria control through multi-sectoral coordination and empowered community participation going forward. The findings indicated that the unstandardized beta coefficient for household wealth index representing the expected change in malaria prevalence associated with a one-unit increase in the measure of socioeconomic position had a value of 0.578, and a standard error of 0.078, furthermore, the standardized beta coefficient quantifying the predictor's relative importance in the model was 0.686, indicating household wealth index made a strong unique contribution to explaining variations in disease endemicity, and the t-statistics and significance levels reveal household wealth index reliably predicted malaria prevalence rates beyond chance. Institutionalize malaria surveillance focal persons at parish level to strengthen timely reporting, active case finding, defaulter tracing and immediate response to localized outbreak risks.