Multimodal AI research has overwhelmingly focused on high-resource languages, hindering the democratization of advancements in the field. To address this, we present AfriCaption, a comprehensive framework for multilingual image captioning in 20 African languages and our contributions are threefold: (i) a curated dataset built on Flickr8k, featuring semantically aligned captions generated via a context-aware selection and translation process; (ii) a dynamic, context-preserving pipeline that ensures ongoing quality through model ensembling and adaptive substitution; and (iii) the AfriCaption model, a 0.5B parametervision-to-text architecture that integrates SigLIP and NLLB200 for caption generation across underrepresented languages. This unified framework ensures ongoing data quality and establishes the first scalable image-captioning resource for underrepresented African languages, laying the groundwork for truly inclusive multimodal AI.