The development of sixth-generation (6G) terahertz (THz) wireless systems requires equalization techniques that can effectively handle severe channel impairments while maintaining low computational complexity. In this work, we propose a hybrid equalization framework that fuses regularized zero-forcing (ZF) with maximum likelihood (ML) refinement for ultra-massive multiple-input multiple-output (UM-MIMO) systems. The proposed Regularized ZF and ML Fusion (RZF-ML) equalizer leverages a regularization factor to mitigate noise enhancement and ill-conditioned channel effects, followed by a lightweight ML-based candidate search that refines symbol detection. This design provides a trade-off between the simplicity of linear equalizers and the optimality of ML detection. Simulation results under Rayleigh and Rician fading channels with high-order quadrature amplitude modulation (QAM) demonstrate that the RZF-ML equalizer achieves significantly improved bit error rate (BER) performance compared to conventional ZF and minimum mean square error (MMSE) equalizers, while approaching ML detection accuracy at a fraction of its complexity. The findings suggest that the proposed method is a promising candidate for robust equalization in 6G THz UM-MIMO networks, enabling reliable high-capacity communication in challenging propagation environments.