Natural Language Processing (NLP) is becoming a dominant subset of artificial intelligence as the need to help machines understand human language looks indispensable. Several NLP applications are ubiquitous, partly due to the myriads of datasets being churned out daily through mediums like social networking sites. However, the growing development has not been evident in most African languages due to the persisting resource limitation, among other issues. Yor\`ub\'a language, a tonal and morphologically rich African language, suffers a similar fate, resulting in limited NLP usage. To encourage further research towards improving this situation, this systematic literature review aims to comprehensively analyse studies addressing NLP development for Yor\`ub\'a, identifying challenges, resources, techniques, and applications. A well-defined search string from a structured protocol was employed to search, select, and analyse 105 primary studies between 2014 and 2024 from reputable databases. The review highlights the scarcity of annotated corpora, limited availability of pre-trained language models, and linguistic challenges like tonal complexity and diacritic dependency as significant obstacles. It also revealed the prominent techniques, including rule-based methods, among others. The findings reveal a growing body of multilingual and monolingual resources, even though the field is constrained by socio-cultural factors such as code-switching and desertion of language for digital usage. This review synthesises existing research, providing a foundation for advancing NLP for Yor\`ub\'a and in African languages generally. It aims to guide future research by identifying gaps and opportunities, thereby contributing to the broader inclusion of Yor\`ub\'a and other under-resourced African languages in global NLP advancements.

翻译：自然语言处理（NLP）正成为人工智能的主导子领域，因为让机器理解人类语言的需求显得不可或缺。众多NLP应用已无处不在，部分原因在于社交媒体等平台每日产生海量数据集。然而，由于持续的资源限制等问题，大多数非洲语言并未明显受益于这一发展浪潮。约鲁巴语作为一种声调丰富且形态复杂的非洲语言，面临相似困境，导致其NLP应用受限。为鼓励进一步研究以改善此状况，本系统性文献综述旨在全面分析针对约鲁巴语NLP发展的研究，识别其挑战、资源、技术与应用。我们采用结构化方案中明确定义的检索字符串，从权威数据库中检索、筛选并分析了2014年至2024年间的105项核心研究。综述指出：标注语料库稀缺、预训练语言模型有限，以及声调复杂性和变音符号依赖等语言学挑战是主要障碍；同时揭示了以规则方法为代表的常用技术。研究发现，尽管该领域受语码转换和数字场景语言弃用等社会文化因素制约，但单语与多语资源体系正在逐步形成。本综述整合了现有研究，为推进约鲁巴语乃至非洲语言的NLP发展奠定基础，通过识别研究空白与机遇指引未来方向，从而促进约鲁巴语及其他资源匮乏的非洲语言更广泛地融入全球NLP发展进程。