The article emphasizes the need for systems-thinking and One Health approaches to understand and address the multiscale dissemination of antimicrobial resistance (AMR). AMR, which occurs not only in clinical settings but also in environmental subsystems such as farms, waterways, and wildlife, requires a comprehensive understanding of how anthropogenic activities interact within these environments. The authors argue that innovative computational methods integrating big data from various sources (clinical, agricultural, and environmental) can accelerate our understanding of AMR and support decision-making. However, challenges exist in accessing, integrating, and interpreting complex, multidimensional datasets, including data confidentiality, geopolitical and cultural variations, and surveillance gaps. Combining systems-thinking with modeling can help explore, scale up, and extrapolate existing data, providing vital insights into the dynamic movement and transmission of AMR. This approach is crucial for developing strategies to slow down AMR dissemination and comparing their efficacy and cost-effectiveness. The article also highlights the importance of considering the environment as a key element in the AMR system, which can be addressed through holistic, systems-thinking approaches. These methods can identify potential control points and interventions, leading to more informed policy decisions and cost-effective mitigation strategies.The article emphasizes the need for systems-thinking and One Health approaches to understand and address the multiscale dissemination of antimicrobial resistance (AMR). AMR, which occurs not only in clinical settings but also in environmental subsystems such as farms, waterways, and wildlife, requires a comprehensive understanding of how anthropogenic activities interact within these environments. The authors argue that innovative computational methods integrating big data from various sources (clinical, agricultural, and environmental) can accelerate our understanding of AMR and support decision-making. However, challenges exist in accessing, integrating, and interpreting complex, multidimensional datasets, including data confidentiality, geopolitical and cultural variations, and surveillance gaps. Combining systems-thinking with modeling can help explore, scale up, and extrapolate existing data, providing vital insights into the dynamic movement and transmission of AMR. This approach is crucial for developing strategies to slow down AMR dissemination and comparing their efficacy and cost-effectiveness. The article also highlights the importance of considering the environment as a key element in the AMR system, which can be addressed through holistic, systems-thinking approaches. These methods can identify potential control points and interventions, leading to more informed policy decisions and cost-effective mitigation strategies.