INTRODUCTION: In Senegal, the Routine Health Information System (RHIS) captures the majority of data from the Ministry of Health and Social Action (MHSA) public structures and very little health data from the private sector and other ministerial departments. Quality data strengthens the validity and reliability of research results. Common areas of data quality include accuracy, completeness, consistency, credibility, and timeliness. The work aims to assess the quality of routine maternal, child, and adolescent health data in Senegal. MATERIALS AND METHODS: A mixed quantitative and qualitative design was chosen in four health districts, including Thiès, Mbour, Kédougou, and Saraya. The study included functional health structures that produce maternal, child, and adolescent health data. For the quantitative part, a descriptive and analytical study was carried out. Lot Quality Assurance Sampling (LQAS) was used as the sampling method. Data were collected using Performance of Routine Information Systems Management (PRISM) data collection tools and the ODK application and analyzed (univariate and bivariate) using R and Stata with an alpha risk of 5%. The following data quality indicators (accuracy, completeness, and promptness) were estimated. An exploratory case study and purposive sampling supported the qualitative part by implementing individual interviews. RESULTS: The study showed an accuracy ratio of 1 in the intervention districts, a difference in the control districts, and a disparity in the transmission of guidelines between districts (inter- and intra-region). The average level of completeness was 0.64 (+/- 0.44) for all regions combined, with no significant difference between districts. The promptness rate for Kédougou, Saraya, Thiès, and Mbour districts was 81%, 75.9%, 72.2%, and 86.7%, respectively. Between 40% and 60% of facilities in each district carried out self-assessments. Data collection tools were considered to be numerous. A large number of tools were easy to use. The recording space was appreciated. On the other hand, the length of the forms was little or not appreciated by the providers. Few of the providers in the 4 districts had been trained to record data in DHIS2. CONCLUSION: Assessment of data quality in the districts studied shows shortcomings in terms of completeness and timeliness. Many factors influence the SMEA data quality situation, including knowledge or application of RHIS policies, standards, and protocols, perception of the importance of RHIS, ease of use of data collection tools, training of providers, and diversity of data production sources.