The relentless advancement of modern technology has given rise to increasingly intricate and sophisticated engineering systems, which in turn demand more reliable and intelligent fault diagnosis methods. This paper presents a comprehensive review of fault diagnosis in uncertain environments, focusing on innovative strategies for intelligent fault diagnosis. To this end, conventional fault diagnosis methods are first reviewed, including advances in mechanism-driven, data-driven, and hybrid-driven diagnostic models and their strengths, limitations, and applicability across various scenarios. Subsequently, we provide a thorough exploration of multi-source uncertainty in fault diagnosis, addressing its generation, quantification, and implications for diagnostic processes. Then, intelligent strategies for all stages of fault diagnosis starting from signal acquisition are highlighted, especially in the context of complex engineering systems. Finally, we conclude with insights and perspectives on future directions in the field, emphasizing the need for the continued evolution of intelligent diagnostic systems to meet the challenges posed by modern engineering complexities.