Aim: The objective of this research paper is the data mining technique to develop the financial development on foreign direct investments by using Apriori algorithm in comparison with K-means clustering. Materials & Methods: The dataset in this paper utilizes the publicly available dataset from National financial development to prove the effectiveness of the approach. The sample size of the data mining technique to develop the financial development on foreign direct investments was sample 280 (Group 1=140 and Group 2 =140) and calculation is performed utilizing G-power 0.8 with alpha and beta qualities are 0.05, 0.2 with a confidence interval at 95%. The data mining technique to develop the financial development on foreign direct investments is performed by Apriori algorithm whereas number of samples (N=10) and K-means clustering where number of samples (N=10). Results: The Apriori algorithm classifier has a 95.68 higher accuracy rate when compared to the accuracy rate of the K-means clustering is 91.346. The study has a significance value p=0.029 (p<0.05), which shows that two groups are statistically significant. Conclusion: Apriori algorithm provides better outcomes in accuracy rate when compared to K-means clustering for the data mining technique to develop the financial development on foreign direct investments.