As Edoardo says in their excellent answer, the task at hand can be approached as an outpainting problem and there's some great tools available to do this.
To throw an alternative into the ring, I'd point to an example in the field of texture synthesis - Self-Organising Textures built with Neural Cellular Automata.
The theory revolves around teaching a very small neural network to generate an image using learned, local update rules. When given a loss function that compares the style of two images, the model can generate textures that seamlessly extend the original.
Within the Self-Organising Texture article, there's a a Google Colab which allows you to import a target image and train the model to reproduce it. I used your image as the target, and it was able to quickly (<20 minutes) make a model that captured the overall pattern of your image:
There are options for refining the resulting texture with different loss functions, and even exerting a degree of artistic control using relative noise levels in the generation process. One of the creators of the models, Alexander Mordvintsev, has an excellent YouTube channel where he walks through some of these techniques and I'd highly recommend checking it out if you want to pursue using this method. Have fun!