This experiment models the implementation of distance metrics and three-way decisions for K-Nearest Neighbor classification (K-NN). K-NN as a machine learning method has inherent classification deficits due to high computing power, outliers, and the curse of dimensionality. Many researchers have experimented and found that a combination of various algorithmic methods can lead to better results in prediction and forecasting fields. In this experimentation, we used the combination and strengths of the Euclidean metric distance to develop and evaluate computing query distance for nearest neighbors using weighted three-way decision to model a highly adaptable and accurate K-NN technique for classification. The implementation is based on experimental design method to ascertain the improved computed Euclidean distance and weighted three-way decision classification to achieve better computing power and predictability through classification in the K-NN model. Our experimental results revealed that distance metrics significantly affects the performance of K-NN classifier through the choice of K-values. We found that K-value on the applied datasets tolerate noise levels to ascertain degree while some distance metrics are less affected by the noise levels. This experiment primarily focused on the findings that best K-value from distance metrics measure guarantees three-way K-NN classification accuracy and performance. The combination of best distance metrics and three-way decision model for K-NN classification algorithm has shown improved performance as compared with other conventional algorithm set-ups making in more ideal for classification in the context of this experiment. It outperforms K-NN, ANN, DT, NB, and the SVM from the crop-yielding datasets applied in the experiment.