Lower Respiratory Tract Infection (LRTI) is a common infection among children in both tropical and subtropical regions which includes Africa, America and Asia. World Health Organization reported more than 2.5 million of deaths as a result of LRTI in 2012, late and untimely diagnosis of this infection is one of the factors responsible for its high mortality rate. This paper employed the use of machine learning techniques to diagnose the presence of LRTI in infants. The LRTI dataset obtained from Federal Medical centre (FMC) Owo in Ondo State was preprocessed and relevant attributes obtained from it as well as the whole preprocessed dataset were used to implement a Naïve bayes and K- nearest neighbor machine learning models using java programming language. The performance of the models were evaluated based on accuracy, sensitivity, specificity and precision. The result of Naïve bayes and k-nearest neighbour with all features (18) used shows 94.25% and 94.43% respectively. Naïve Bayes with information- based feature selection method shows accuracy of 99.60% while k-nearest neighbour shows 94.35% with 10 features. Also, Naïve Bayes with Correlation-based feature selection method shows accuracy of 95.40% while k-nearest neighbour shows 95.40% too with just six (6) features. The comparative results shows that Naïve bayes with information- based feature selection method performs stronger and better than others.