Despite the popularity and utility of most machine learning techniques, expert knowledge is required in guiding choices about the suitable technique and settings that are good for solving a specific problem. The lack of expert information renders the procedures vulnerable to poor parameter settings. Several of these machine learning techniques configurations are offered under default settings. However, since different classification problems required suitable machine learning techniques, selecting the appropriate technique and tuning its settings are vital works that will rightly improve predictions in terms of reliability and accuracy. This study aims to perform grid search parameters tuning on 5-selected machine learning techniques on hepatitis disease. Comparative performance is drawn side-by-side with the default settings. The experimental results of the five tuning techniques show that using the configurations suggested in our work yield predictions of a greatly sophisticated quality than choice under its default settings. The result proves that tuning parameters of Support Vector Machine via grid search yields the best accuracy outcomes of 90% and has a competitive performance relative towards criteria of precision, recall, accuracy and Area Under the Curve. Present combinations of parameter settings for each of the techniques by identifying ranges of values for each setting that give good Hepatitis disease outcomes