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Multifractal Detrended Fluctuation Analysis (MFDFA) is a powerful and widely used technique for characterizing the scaling properties and long-range correlations of complex time series. However, its application often involves significant practical challenges, such as the subjective identification of scaling regions (crossovers) and the disambiguation of the physical origins of multifractality. We introduce MF-toolkit, a high-performance, parallelized Python library designed to address these challenges. It integrates three key innovations: (1) fully automatic crossover detection algorithms (CDV-A and SPIC), which remove operator bias and enhance reproducibility; (2) a built-in implementation of the Iterative Amplitude Adjusted Fourier Transform (IAAFT) for generating surrogate data, enabling the robust identification of the source of multifractality; and (3) a comprehensive suite for generating synthetic time series for rigorous validation. We demonstrate the rigor and utility of MF-toolkit through its application to characterize the multifractal properties of non-stationary noise in gravitational wave (LIGO) data. The MF-toolkit library offers a robust, efficient, and user-friendly tool for advanced time series analysis, facilitating more rigorous and reproducible research across physics and other data-intensive fields. PROGRAM SUMMARY/NEW VERSION PROGRAM SUMMARY Program Title: MF-toolkit CPC Library link to program files:https://doi.org/10.17632/zwx686b23t.1 Developer's repository link:https://github.com/NahueMendez/mf-toolkit Licensing provisions: MIT Programming language: Python Supplementary material:https://app.readthedocs.org/projects/mf-toolkit/ Nature of problem: Applying multifractal detrended fluctuation analysis to a time series is a widely used process in complex system physics but the time computing and the resources needed strongly depends on the number of samples of the input signal. The method in its multifractal extension require to calculate the fluctuation function for every scale size s and every moment q. This issue escalates when it is needed to analyze a group of time series to asses statistically its multifractals parameters. Solution method: We address the bottleneck of MFDFA by computing the fluctuation functions using CPU-based plane parallelization and Numba's just-in-time compilation taking advantage of the independency of the calculations. This drastically reduces time computing, and it can be used for processing large datasets.