Meningitis is a deadly disease which affects the Meninges that covers the brain and the spinal cord. The report from WHO indicated that every year, bacterial meningitis epidemics affect more than 400 million people living in the 26 countries of the extended "African meningitis belt" (from Senegal to Ethiopia). Moreover, it is reported from existing studies that for every 5 to 12 years, there is an attack rates reaching 1000 cases per 100,000 population in the region of meningitis belt. In Nigeria, a total of 13,420 suspected cases of Cerebrospinal Meningitis (CSM) were reported from 23 states and the death toll was 1,069; hence giving a case fatality ratio of eight per cent. In March 2018 in the “meningitis belt” of Northern Nigeria, a total of 925 suspected meningitis cases as against 537 in March 2017and with 57 deaths and 60 deaths respectively were reported. Though lots of conventional clinical efforts are put in by the Nigerian government and the international communities to curb this disease, however new cases emerge and deaths are recorded.(we need to also mention the existence of computational methods, before we can now talk about comparision) Hence the need for a research that presents a comparative analysis of conventional clinical and computational methods for diagnosing meningitis. Our results established that clinical diagnostic methods is having challenges as regards speed and accuracy of diagnosis; misdiagnosis due to confusability nature of the symptoms with other related disease and hence elongates the diagnostic process; lack of early diagnosis and dearth of paediatricians. Also, we could not identify presently a diagnostic model or software that is developed in Nigerian context for meningitis( Are there software that have been developed else to solve this problem? If yes, we need to present the demerits of such asa reason why we cannot adapt it and therefore show the need for one to solve the Nigerian Probem). Thus, we established based on our deductions that future research should focus on the development and implementation of computational meningitis diagnostic models or software to carry out proper differential analysis of meningitis symptoms during diagnosis and hence increase the speed and accuracy of diagnosis.