Deep learning models have achieved remarkable success across various domains, yet a comprehensive theoretical understanding of their generalization capabilities remains an active area of research. Traditional Probably Approximately Correct (PAC) learning theory provides rigorous bounds on generalization errors but often struggles to explain the excellent performance of overparameterized deep networks. This paper proposes a novel framework that bridges PAC theory with insights from algebraic topology to explore the learnability and generalization properties of deep neural networks. We postulate that the topological features of data manifolds, loss landscapes, and neural network function spaces play a crucial role in determining model complexity and generalization. Specifically, we investigate how concepts like persistent homology and Betti numbers can quantify the "shape" and connectivity of these spaces, offering new perspectives on model capacity and the robustness of learned representations. By integrating these topological invariants into a PAC-learning framework, we aim to develop a more nuanced understanding of why deep networks generalize effectively, particularly in scenarios where classical complexity measures fall short. This theoretical exploration paves the way for new avenues of research in designing more robust and interpretable deep learning architectures.