Artificial intelligence (AI) technologies are increasingly transforming academic research workflows, particularly in literature search and review processes. Understanding the opportunities, applications, and ethical implications of AI integration in systematic evidence synthesis is critical for responsible adoption in scholarly practice. This systematic review aimed to comprehensively examine the current state of AI applications in literature search and review. We conducted comprehensive searches across multiple databases (SciSpace, Google Scholar, ArXiv, PubMed) from inception to April 2026. Studies were eligible if they addressed AI applications in literature search, screening, data extraction, synthesis, or ethical considerations in academic research. The protocol of this review was published in the Biomedical Journal of Scientific & Technical Research. Following PRISMA 2020 guidelines, we screened 413 unique papers, with 90 papers meeting inclusion criteria for detailed synthesis. Data extraction focused on study design, AI tools and technologies, key findings, performance outcomes, and ethical considerations. AI tools demonstrated substantial benefits in automating literature review tasks, including time savings (median 47% reduction in screening burden), enhanced efficiency in search and screening, and improved synthesis capabilities. Key applications included automated screening (DistillerSR, Abstrackr, ASReview), natural language processing for data extraction (spaCy, transformer models), and large language models for synthesis (ChatGPT, GPT-3.5, GPT-4). However, significant ethical concerns emerged, including risks of algorithmic bias, over-reliance leading to diminished critical thinking, transparency challenges, academic integrity threats (plagiarism, fabricated references), and issues with authorship attribution. Performance varied by task complexity, with AI excelling in high-volume screening but requiring human oversight for nuanced interpretation. AI technologies offer transformative potential for literature search and review, significantly enhancing the efficiency and scalability of evidence synthesis. However, responsible integration requires robust ethical frameworks emphasizing transparency, human oversight, critical evaluation of AI outputs, and adherence to academic integrity standards. AI should function as a collaborative partner augmenting human expertise rather than replacing scholarly judgment. Future research should focus on developing standardized reporting guidelines, validation frameworks, and institutional policies for ethical AI adoption in systematic reviews.