The inverse problem, aiming at determining the unknown cause given an observed effect, is a fundamental challenge in scientific investigations. In the field of high-energy physics, understanding the complexities of quantum chromodynamics (QCD) relies on analyzing multi-dimensional quantum correlation functions (QCFs), which are derived from experimentally observed events. While the mapping from parameters to observable events in QCFs is a well-posed problem with unique solutions, similar to a general inverse problem of deriving parameters from observables, the inverse problem of inferring parameters from observed events, poses unique challenges due to its ill-posedness. This paper introduces a machine learning-based framework based on generative adversarial networks (GANs), the so-called GAN-based Event-level Inverse Mapper (GEIM), which is designed to address the inverse problem of femtoscale imaging in QCD. GEIM consists of two GANs: the conditional GAN-based\textit {surrogate event generator}, which replaces the physics-based QCF model to generate synthetic events, and the\textit {outer-GAN}, which performs the backward mapping to derive the parameter distributions. Through a proxy 1D QCF analysis, we demonstrate the efficacy of GEIM in accurately learning the mapping between observable events and QCF parameter spaces, deriving QCF parameters from event-level analysis, and eventually reconstructing QCFs.