Abstract Citizen science (CS) has emerged as a collaborative process for addressing complex scientific and societal challenges. The emergence of artificial intelligence (AI) into CS projects, has transformed data collection, analysis, and validation steps. However, significant gaps remain in understanding the methodologies, applications, and challenges of AI-CS integration. Our systematic review seeks to address the gaps by answering three questions: (1) What AI methodologies are most commonly applied in CS projects? (2) How does AI integration impact the efficiency and scalability of CS initiatives? (3) What challenges arise from AI-CS integration, and how are they mitigated? Following the PRISMA-ScR guidelines, a systematic search of Scopus, ACM Digital Library, and Web of Science identified relevant articles published between 2013 and 2024. From an initial pool of 2,470 publications, 90 were retained after filtering through the eligibility criteria. Our findings illustrate ML techniques, including deep learning, clustering algorithms, and convolutional neural networks, boost data annotation, classification, and validation in applications across various disciplines. How
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