  Mittag-Leffler correlated noise (M-L noise) plays a crucial role in the
dynamics of complex systems, yet the scientific community has lacked tools for
its direct generation. Addressing this gap, our work introduces GenML, a Python
library specifically designed for generating M-L noise. We detail the
architecture and functionalities of GenML and its underlying algorithmic
approach, which enables the precise simulation of M-L noise. The effectiveness
of GenML is validated through quantitative analyses of autocorrelation
functions and diffusion behaviors, showcasing its capability to accurately
replicate theoretical noise properties. Our contribution with GenML enables the
effective application of M-L noise data in numerical simulation and data-driven
methods for describing complex systems, moving beyond mere theoretical
modeling.
