Diabetes Mellitus (DM) is one of the most chronic and debilitating diseases in the modern society and represents not only a medical problem, but also a socio-economic problem. Computational Intelligence Techniques (CIT) have been successfully employed in diabetes disease diagnosis, risk evaluation, patient monitoring and prediction in medical field. Using single techniques in the diagnosis of diabetes has been comprehensively investigated showing some level of accuracy in Fuzzy Logic (FL) and Artificial Neural Network (ANN) for diagnosis of diabetes mellitus. Therefore, this study aimed at developing a Genetic Neuro Fuzzy Inferential System (GNFIS) for the diagnosis of diabetes. The objectives were to: (i) develop an enhanced hybrid system for diagnosis of diabetes mellitus using genetic algorithm; (ii) implement the enhanced system using Java programming language; (iii) evaluate the performance of the proposed system based on accuracy, sensitivity and specificity; and (iv) perform a comparison of the proposed system with two existing systems; Fuzzy Logic (FL) and Artificial Neural Network (ANN) for diagnosis of diabetes mellitus.rnrnThe Neuro fuzzy inferential system was used to classify diabetes mellitus with genetic algorithm applied to obtain most relevant attributes from Pima Indian Diabetes Dataset (PIDD). Direct rating method was used for acquiring data used for the system. These data were presented in a series of objects to a domain expert who was requested to rate the membership function for the eight most significant attributes which also serves as input to the system. The ratings were aggregated for membership function calculation. The lowest was used as the minimum and highest as the maximum values. Triangular membership function was used because of its flexibility and fewer complexities when splitting values (low, medium and high), compared to other membership functions.rnrnThe findings of the study were that:rni. the enhanced hybrid system using genetic algorithm performed better during the diagnosis process for diabetes mellitus;rnii. the enhanced system was implemented using Java Programming Language and it achieved a high level of accuracy;rniii. the GNFIS gave a minimum diagnosis accuracy of 98.26%, maximum diagnosis accuracy of 99% andthe averageaccuracyof97.76%; sensitivity of 96% and specificity of 99% for the reduced dataset; andrniv. results of comparison showed that GNFIS had a better performance with 99.34% accuracy on the whole dataset used when compared with FL and ANN with 96.14% and 95.14% respectively. rnrnThe study concluded that the genetic algorithm is a good attributes reduction technique for Neuro Fuzzy Inferential System. The developed GNFIS performed efficiently and also outperformed the existing systems. Thus, the study recommended GNFIS as a good technique for the screening and diagnosis of DM.