The article provides a comprehensive review of fuzzy machine learning (FML), integrating various fuzzy techniques such as fuzzy sets, fuzzy systems, fuzzy logic, and fuzzy measures to address the limitations of traditional machine learning in uncertain and complex environments. The review is divided into five categories: fuzzy classical machine learning, fuzzy transfer learning, fuzzy data stream learning, fuzzy reinforcement learning, and fuzzy recommender systems. Each category is discussed in detail, highlighting recent advancements and applications. The authors aim to provide researchers with a solid understanding of the current progress and future directions in FML, emphasizing its robustness and interpretability in handling uncertainties and complex data structures. The article also includes a systematic literature review, selecting and analyzing relevant studies from high-quality journals and conference proceedings, and outlines the main contributions of the review, including a critical discussion of state-of-the-art models and future research directions.The article provides a comprehensive review of fuzzy machine learning (FML), integrating various fuzzy techniques such as fuzzy sets, fuzzy systems, fuzzy logic, and fuzzy measures to address the limitations of traditional machine learning in uncertain and complex environments. The review is divided into five categories: fuzzy classical machine learning, fuzzy transfer learning, fuzzy data stream learning, fuzzy reinforcement learning, and fuzzy recommender systems. Each category is discussed in detail, highlighting recent advancements and applications. The authors aim to provide researchers with a solid understanding of the current progress and future directions in FML, emphasizing its robustness and interpretability in handling uncertainties and complex data structures. The article also includes a systematic literature review, selecting and analyzing relevant studies from high-quality journals and conference proceedings, and outlines the main contributions of the review, including a critical discussion of state-of-the-art models and future research directions.