The plastic flow behaviors of AA6061-T4 sheets at different temperatures (21−300 °C) and strain rates (0.002−4 s⁻¹) were studied. Significant nonlinear effects of temperature and strain rate on flow behaviors were revealed, as well as underlying micromechanical factors. Phenomenology and machine learning-based constitutive models were developed. Both models were formulated in the framework of a temperature-dependent linear combination regulated by a transition function to capture the evolution of strain-hardening behavior with increasing temperature. Novel mathematical functions for describing temperature and strain rate sensitivities were formulated for the phenomenological constitutive model. The threshold temperature related to microstructure evolution was considered in the modeling. A data-enrichment strategy based on extrapolating experimental data via classical strain hardening laws was adopted to improve neural network training. An efficient inverse identification strategy, focusing solely on the transition function, was proposed to enhance the prediction accuracy of post-necking deformation by both constitutive models.