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Gene fusions are vital biomarkers for tumor diagnosis and drug development, with precise detection becoming increasingly important. This review explores the links between gene fusions and common tumors, systematically evaluating detection technologies like fluorescence in situ hybridization (FISH), polymerase chain reaction (PCR), immunohistochemistry (IHC), electrochemiluminescence (ECL), and next-generation sequencing (NGS). FISH is the gold standard for DNA-level rearrangements, while PCR and NGS are widely used, with PCR confirming known fusions and NGS offering comprehensive genome-wide detection. Bioinformatic tools like STAR-Fusion, FusionCatcher, and Arriba are assessed for diagnostic accuracy. The review highlights how artificial intelligence (AI), particularly deep learning (DL) technologies like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), is transforming gene fusion research by accurately detecting and annotating genes from genomic data, eliminating biases. Finally, we present an overview of advanced technologies for gene fusion analysis, emphasizing their potential to uncover unknown gene fusions. Highlights: • This review explores the intricate relationship between gene fusions and prevalent tumors, comprehensively reviewing techniques for gene fusion testing. • It evaluates the strengths and limitations of key assays like fluorescence in situ hybridization, polymerase chain reaction, and next-generation sequencing, and explores the emerging use of electrochemiluminescence for high-sensitivity gene fusion detection. • It emphasizes the diagnostic accuracy of bioinformatics tools such as STAR-Fusion, FusionCatcher, Arriba, and underscores the transformative role of artificial intelligence, particularly deep learning technologies, in revolutionizing gene fusion research. [ABSTRACT FROM AUTHOR]