Epithelial-to-mesenchymal transition (EMT) is a critical process in cancer metastasis, enabling tumor cells to detach from epithelial tissues, invade, and spread to distant sites. While EMT was initially viewed as a binary process, recent research reveals a spectrum of EMT states, including partial, intermediate, extreme, and ameboid EMT, each with distinct roles in metastasis. These states are characterized by varying degrees of cellular plasticity, stemness, immune evasion, and chemoresistance. The emergence of single-cell technologies and spatial analysis has revealed the complexity of EMT, highlighting the importance of hybrid EMT states in aggressive metastasis. However, the exact mechanisms and functional roles of different EMT types remain unclear, posing significant challenges for cancer therapy. EMT is not only a driver of metastasis but also influences tumor progression through properties such as stemness, immunosuppression, and drug resistance. Recent studies have shown that EMT can occur at various stages of cancer development, and its role in metastasis is context-dependent. The plasticity of EMT states allows tumor cells to adapt to different microenvironments, making them more resistant to treatment. Understanding the molecular and cellular mechanisms underlying EMT is crucial for developing effective therapies targeting metastasis. Targeting EMT in cancer therapy involves several strategies, including inhibiting EMT drivers, limiting cellular plasticity, and promoting differentiation into epithelial or terminally mesenchymal states. Therapeutic approaches such as MEK inhibitors and anti-diabetic drugs have shown promise in reducing metastasis by forcing EMT cells into post-mitotic states. However, challenges remain in defining and measuring EMT states, as well as in translating these findings into clinical applications. The development of new technologies, such as single-cell lineage tracing and machine learning, will be essential for advancing EMT research and improving cancer treatment outcomes. Overall, the complexity of EMT in cancer metastasis underscores the need for interdisciplinary efforts to unravel its mysteries and develop effective therapeutic strategies.Epithelial-to-mesenchymal transition (EMT) is a critical process in cancer metastasis, enabling tumor cells to detach from epithelial tissues, invade, and spread to distant sites. While EMT was initially viewed as a binary process, recent research reveals a spectrum of EMT states, including partial, intermediate, extreme, and ameboid EMT, each with distinct roles in metastasis. These states are characterized by varying degrees of cellular plasticity, stemness, immune evasion, and chemoresistance. The emergence of single-cell technologies and spatial analysis has revealed the complexity of EMT, highlighting the importance of hybrid EMT states in aggressive metastasis. However, the exact mechanisms and functional roles of different EMT types remain unclear, posing significant challenges for cancer therapy. EMT is not only a driver of metastasis but also influences tumor progression through properties such as stemness, immunosuppression, and drug resistance. Recent studies have shown that EMT can occur at various stages of cancer development, and its role in metastasis is context-dependent. The plasticity of EMT states allows tumor cells to adapt to different microenvironments, making them more resistant to treatment. Understanding the molecular and cellular mechanisms underlying EMT is crucial for developing effective therapies targeting metastasis. Targeting EMT in cancer therapy involves several strategies, including inhibiting EMT drivers, limiting cellular plasticity, and promoting differentiation into epithelial or terminally mesenchymal states. Therapeutic approaches such as MEK inhibitors and anti-diabetic drugs have shown promise in reducing metastasis by forcing EMT cells into post-mitotic states. However, challenges remain in defining and measuring EMT states, as well as in translating these findings into clinical applications. The development of new technologies, such as single-cell lineage tracing and machine learning, will be essential for advancing EMT research and improving cancer treatment outcomes. Overall, the complexity of EMT in cancer metastasis underscores the need for interdisciplinary efforts to unravel its mysteries and develop effective therapeutic strategies.