This paper presents a comprehensive overview of machine learning (ML) techniques for data unfolding in particle physics. The goal is to estimate the true particle-level distributions from detector-level data, which is essential for precise measurements of Standard Model parameters and potential new physics. The authors evaluate various ML-based unfolding methods on two datasets: Z+jets and top quark pair production. They compare the performance of different approaches, including reweighting, distribution mapping, and generative models. The paper introduces several ML-based unfolding methods. Reweighting methods, such as OmniFold, use classifiers to reweight simulated data to estimate particle-level distributions. Distribution mapping techniques, like Schrödinger Bridge and Direct Diffusion, learn the transformation between detector-level and particle-level data. Generative models, including cINN, Transfermer, CFM, TraCFM, and Latent Diffusion, learn the conditional probability distribution of the underlying physics process. The authors demonstrate that all methods can accurately unfold data from the detector level to the particle level using the Z+jets benchmark dataset. They also show that unfolding to the parton level can provide additional insights into QCD effects. The study highlights the advantages of ML-based unfolding, including the ability to handle high-dimensional data and provide more accurate results than traditional methods. The paper concludes that ML-based unfolding offers a powerful toolkit for particle physics measurements, enabling unprecedented detail in probing the Standard Model and potentially detecting new phenomena. The methods are evaluated on two datasets, and the results show that all techniques are capable of accurately reproducing particle-level spectra across complex observables. The study emphasizes the importance of considering the statistical properties of the data and the need for careful validation of the unfolding results.This paper presents a comprehensive overview of machine learning (ML) techniques for data unfolding in particle physics. The goal is to estimate the true particle-level distributions from detector-level data, which is essential for precise measurements of Standard Model parameters and potential new physics. The authors evaluate various ML-based unfolding methods on two datasets: Z+jets and top quark pair production. They compare the performance of different approaches, including reweighting, distribution mapping, and generative models. The paper introduces several ML-based unfolding methods. Reweighting methods, such as OmniFold, use classifiers to reweight simulated data to estimate particle-level distributions. Distribution mapping techniques, like Schrödinger Bridge and Direct Diffusion, learn the transformation between detector-level and particle-level data. Generative models, including cINN, Transfermer, CFM, TraCFM, and Latent Diffusion, learn the conditional probability distribution of the underlying physics process. The authors demonstrate that all methods can accurately unfold data from the detector level to the particle level using the Z+jets benchmark dataset. They also show that unfolding to the parton level can provide additional insights into QCD effects. The study highlights the advantages of ML-based unfolding, including the ability to handle high-dimensional data and provide more accurate results than traditional methods. The paper concludes that ML-based unfolding offers a powerful toolkit for particle physics measurements, enabling unprecedented detail in probing the Standard Model and potentially detecting new phenomena. The methods are evaluated on two datasets, and the results show that all techniques are capable of accurately reproducing particle-level spectra across complex observables. The study emphasizes the importance of considering the statistical properties of the data and the need for careful validation of the unfolding results.