Kernel pca and kernel svm PCA is a way to reduces dimension and complexities, but is it ok to use kernel PCA with radial basis function and then use kernel SVM using the same.

Yes, why not. It's not different from using an ML pipeline with steps [PCA, SVM] cascaded. The kernel trick only adds some nonlinearity to these steps/algorithms.
