INTRODUCTION:
Malaria remains one of the leading causes of morbidity and mortality in sub-Saharan Africa, disproportionately affecting vulnerable populations. Despite progress, inequities in prevention and treatment access persist. Context and Aim This study explores how data analytics can be applied to malaria surveillance to identify gaps in access, improve targeting of interventions, and support equitable malaria control strategies in Nigeria. The aim is to demonstrate how evidence-based insights can shape policy and strengthen health equity.

METHODS:
We analyzed national malaria surveillance datasets (2015–2023) using Excel, SQL, and Power BI. Data cleaning and visualization techniques were applied to highlight spatial and demographic disparities. Key indicators included case incidence, insecticide-treated net distribution, and access to antimalarial treatment.

RESULTS:
Preliminary findings revealed persistent regional disparities, with rural communities experiencing higher incidence rates and limited preventive coverage. Data visualization uncovered inequities in health resource allocation, with urban centers receiving proportionately more interventions. These patterns underscore systemic challenges in achieving equitable malaria control. Innovative Contribution to Policy, Practice, and/or Research By integrating data analytics into malaria surveillance, this study provides an innovative approach for identifying inequities and informing resource distribution. The findings highlight the potential of digital health tools to guide policy, improve efficiency, and ensure interventions reach the populations most in need. The study contributes to global discussions on equity, inclusion, and sustainability by demonstrating how data-driven strategies can bridge gaps in malaria control.