<p>Corpus linguistics (CL) now forms the empirical backbone of many artificial intelligence (AI) language technologies. However, the reciprocal influence of these two domains remains critically under-described in studies from emerging regions. To address this gap, this present study offers a global bibliometric map of open-access research situated at the CL–AI intersection between 2020 and mid-2025. It aims to expose thematic evolution, geographical leadership and conceptual blind spots. Adopting a quantitative bibliometric approach, we sequentially combined performance indicators, network visualisations and keyword clustering. The initial Scopus search yielded 3214 records. After applying the inclusion criteria, removing duplicates and screening for relevance and open-access status, the final dataset comprised 162 documents, which were then normalised and analysed through trend counting, co-citation analysis and co-word mapping. Findings reveal a sharp post-2022 surge in output, accompanied by a thematic pivot from earlier data-mining paradigms to transformer-driven studies. China and the United States dominate both productivity and citations. Nevertheless, early citation visibility is emerging from Bangladesh, Egypt and Malaysia. Bridging keywords such as language models and semantics occupy high betweenness positions. It signals conceptual chokepoints where linguistic theory and AI innovation converge. The results imply that transformer architectures reward curated, linguistically representative corpora, while open-access dissemination and South–North collaborations broaden global impact. Funding bodies are therefore encouraged to incentivise cross-regional partnerships and journals to sponsor special issues that unite computational and pedagogical strands. Future research should extend coverage to paywalled databases, incorporate qualitative expert interviews and pursue longitudinal institutional case studies to gauge how synthetic corpora and collaborative funding reshape the evolving CL–AI landscape.</p>