Systematic reviews and meta-analyses are essential methods in evidence-based medicine for integrating research evidence and guiding clinical decision-making. However, with the rapid expansion of medical research data, traditional approaches face significant challenges in terms of efficiency, accuracy, and reliability. In recent years, the rapid advancement of artificial intelligence (AI) technologies, particularly in natural language processing (NLP), machine learning (ML), and large language models (LLMs), has provided robust support for automating and intelligentizing systematic reviews and meta-analyses. This paper systematically reviews the progress of AI applications in these fields, tracing the evolution from traditional tools to intelligent platforms, and analyzes the functional characteristics, application scenarios, and limitations of existing AI-driven tools. Furthermore, it explores the challenges posed by AI in terms of adaptation to the medical field, multimodal data processing, and ethical transparency, while offering potential solutions and optimization strategies. Looking ahead, with the continuous optimization of technology, enhanced data sharing, and the establishment of industry standards, AI is expected to significantly improve the efficiency and quality of systematic reviews and meta-analyses, driving the transition from "tool-driven" to "intelligent collaboration." The deep integration of AI not only injects innovative momentum into evidence-based medicine but also reshapes its methodological foundation, laying a solid basis for a more intelligent, equitable, and efficient future.