Background and Aim: Myelodysplastic syndromes (MDS) are a heterogeneous group of clonal disorders characterised by ineffective haematopoiesis and an increased risk of progression to acute myeloid leukaemia. In the Democratic Republic of Congo (DRC), healthcare institutions face major diagnostic challenges due to minimal infrastructure, limited access to testing equipment, and a shortage of trained professionals. This study analyses MDS diagnostic practices in the DRC, identifies unmet needs, and proposes strategies for improving early and accurate detection. Methods: A comprehensive literature review was conducted using international databases (PubMed, Google Scholar, and Scopus) alongside local health reports. The research synthesised data from peer-reviewed articles, hospital-based studies, and World Health Organisation (WHO) guidelines, with a focus on MDS diagnosis in sub-Saharan Africa, particularly in the DRC. Results: The findings reveal persistent diagnostic barriers in the DRC, including limited availability of bone marrow aspiration tools, under-resourced laboratories, and a lack of trained hematopathologists. MDS diagnosis largely depends on peripheral blood analysis and basic marrow examinations, leading to frequent underdiagnosis and misclassification. The absence of standardised diagnostic protocols and inconsistent reporting practices further hampers accurate disease identification. Moreover, brown pigmentation in cases of acute myeloid leukaemia can obscure proper diagnosis, underscoring the need for timely and precise detection methods. Conclusion: MDS diagnostic evaluation in the DRC is hindered by systemic and technical limitations, including infrastructure deficits and workforce shortages. Addressing these issues requires strengthening laboratory capacity, expanding access to diagnostic technologies, and investing in specialist training through international collaborations and local educational initiatives. There is an urgent need for a national diagnostic guideline tailored to the DRC’s healthcare context to ensure accurate classification and improve patient outcomes.