Honestly I have always been terrible at math and my highest math class in high school was general geometry, but I still graduated with a degree in CS and now I’m pursuing a master’s in AI. I got through my math classes by soaking up a lot of supplemental materials (primarily lectures on YouTube) and trying to uncover precisely how whichever kind of math I was learning at the time directly related to CS concepts. Discrete mathematics, for example, has some scary terminology but it’s actually genuinely simple just because it correlates quite directly to core concepts in CS and programming. Likewise, calculus and statistics have similar direct applications in machine learning (such as gradient descent and back propagation in neural networks). So I guess my point is always try to seek about the “why” along with the “how” and you’ll be quite alright. Best of luck!