Morphing Attack Detection (MAD) is a critical task in biometric security, aimed at identifying and mitigating the risks posed by morphing attacks, where a face image is manipulated to resemble multiple individuals. Therefore, MAD systems are essential to prevent unauthorized access and ensure the integrity of biometric authentication procedures. However, the acquisition, storing and transfer of real biometric data on which they are based are limited by ethical, legal, and privacy concerns, and this hinders their accuracy. To address these issues, we propose and release MONOT, a new dataset of synthetic morphed images. The dataset includes high-quality synthetic morphed images that are ISO/ICAO compliant and have the characteristics of real biometric data without compromising individual privacy. The morphing procedure is applied through six different morphing algorithms, providing a great level of data variability. Our experimental results demonstrate MONOT morphed images show a high attack potential and that MAD systems trained on MONOT exhibit high detection performance across various morphing techniques. All these elements highlight the dataset’s effectiveness in supporting the development of robust and generalized MAD systems.