Recent advances in quantum computing hardware have significantly outpaced our ability to develop effective quantum algorithms, creating a critical gap between physical qubit scaling and practical quantum advantage. This research investigates the application of generative machine learning models to address fundamental limitations in variational quantum algorithms by automating and optimizing quantum circuit architecture design. We propose a paradigm shift from traditional quantum gradient optimization to distribution learning approaches that can efficiently generate high-quality quantum circuits tailored to specific computational tasks. Our framework leverages various generative modeling techniques—including normalizing flows, diffusion models, and transformers—to learn the complex mapping between problem specifications and effective quantum circuit designs. Rather than treating each molecular structure as an independent optimization problem, our approach develops models that recognize patterns across diverse instances, enabling efficient inference of circuit parameters and architectures. This methodology addresses key challenges in variational quantum computing, including barren plateaus, complex optimization landscapes, and hardware noise sensitivity. By bridging recent breakthroughs in artificial intelligence with quantum computing, we aim to enhance the expressivity, efficiency, and noise robustness of quantum algorithms. Our approach represents a fundamental rethinking of how quantum circuits are designed, potentially accelerating progress toward practical quantum advantage in chemistry simulations and beyond. This research contributes to closing the gap between rapidly advancing quantum hardware capabilities and the algorithmic innovations needed to harness their computational power.