Aim:The main objective of this research article is an enhanced financial development on foreign direct investments using a data mining technique named novel Apriori algorithm in comparison with K-Nearest Neighbor (KNN). Materials & Methods: The dataset in this paper utilizes the publicly available dataset from the National financial development to prove the effectiveness of the approach. The sample size of an enhanced financial development on foreign direct investments was sample 280 (Group 1=140 and Group 2 =140) and Calculation is done using G-power 0.8, and the alpha and beta values are 0.05 and 0.2, respectively, with a 95 percent confidence level.The enhanced financial development on foreign direct investments using data mining techniques is performed by novel Apriori algorithm whereas a number of samples (N=10) and K-Nearest Neighbor (KNN) where a number of samples (N=10). Results: The Apriori algorithm classifier has a 95.68 higher accuracy rate when compared to the accuracy rate of K-Nearest Neighbor (KNN) is 92.46.Two groups are statistically significant in the study, as indicated by the significance value of p=0.035 (p0.05). Conclusion: Apriori algorithm provides a better outcomes inaccuracy rate when compared to K-Nearest Neighbor (KNN) for an enhanced financial development on foreign direct investments using data mining techniques.