This paper introduces KernelEvolve, a framework designed to automate the generation and optimization of kernels for deep learning recommendation models across various hardware platforms. It addresses challenges related to model and kernel diversity by using a graph-based search method for efficient kernel optimization. The framework has been validated on multiple NVIDIA and AMD GPUs and Meta's AI accelerators, achieving high correctness and significantly reducing development time.