In the present paper, it is proved that the interval wavelets neural networks has universal and L² approximation properties and is a consistent function estimator. Convergence rates associated with these properties do not decrease as d increases in d-dimensional function learning, i.e., the “curse of dimensionality” is eliminated substantially. In the experiments, the proposed interval wavelet neural networks, compared to traditional wavelet networks, has performed better.