Figurative speech detection has emerged as a critical task in natural language processing (NLP), enabling machines to comprehend non-literal expressions such as metaphor, irony, and sarcasm. This study presents a systematic literature review with a multilevel analytical framework, examining figurative language across lexical, syntactic, semantic, discourse, and pragmatic levels. We investigate the interplay between feature engineering, model architectures, and annotation strategies across different languages, analyzing datasets, linguistic resources, and evaluation metrics. Special attention is given to the challenges posed by morphologically rich and low-resource languages, where deep learning dominates but rule-based and hybrid approaches remain relevant. Additionally, we discuss methodological trends, limitations, and future research directions, emphasizing the need for multimodal integration and explainable AI techniques. By structuring our analysis through linguistic and computational levels, this review aims to facilitate the development of more robust and inclusive figurative speech detection systems.