A study on vector database and AI integration identifies unstable indexing, weak cross-modal fusion, and rigid resource scheduling as key barriers. By introducing HNSW optimization, unified feature alignment, and dynamic computing allocation, the research improves retrieval accuracy, system responsiveness, and scalability for large-scale AI infrastructure. New York, NY, United States, April 14, 2026 — Vector databases have become a foundational component of modern AI infrastructure, enabling the high-dimensional search and semantic retrieval capabilities that power applications ranging from question answering to image recognition. The study Cutting-Edge Challenges and Solutions for the Integration of Vector Database and AI Technology examines critical barriers in vector database and AI integration, identifying unstable vector index accuracy, inefficient cross-modal feature fusion, and rigid computing resource scheduling as core obstacles. The research proposes targeted solutions using HNSW graph architectures, a unified feature fusion framework, and dynamic resource scheduling to improve retrieval accuracy, response speed, and scalability across production AI systems. Vector database systems face persistent technical challenges that limit the reliability and performance of modern AI applications, including indexing instability under high-dimensional search conditions, misalignment between different data modalities, and inflexible resource allocation models that cannot adapt to variable workloads. The research identifies where current vector database infrastructure falls short and proposes concrete solutions to improve system performance at scale, with applications spanning question answering, image recognition, and multilingual dialogue systems where retrieval accuracy and low latency are critical to reliable operation. The study puts forward three targeted solutions corresponding to each identified challenge. To stabilize vector index performance, the research proposes search structure optimization using Hierarchical Navigable Small World (HNSW) graph architectures, which allow systems to locate similar data points more reliably without scanning entire datasets. To address cross-modal feature fusion, the study introduces a unified integration framework that brings different data representations into semantic