We propose a robust deep mixture model based on a pathway-wise shared scale-mixture construction. Layer-specific component indicators are independently distributed according to their corresponding mixing proportions and jointly define a complete pathway through the latent hierarchy. Conditional on the selected pathway, a single gamma-distributed latent precision variable is shared across the deepest latent distribution, every intermediate latent transition, and the observation model. Integrating out this shared precision yields an exact multivariate Student-$t$ distribution for each complete pathway, allowing robustness to propagate coherently throughout the entire latent hierarchy rather than being introduced separately within individual latent layers. Model parameters are estimated using a stochastic expectation--maximisation algorithm. Complete-pathway responsibilities are evaluated analytically, whereas the shared latent precision variables and latent Gaussian variables are generated from their conditional distributions before updating the model parameters. The pathway-specific degrees-of-freedom parameters are estimated by one-dimensional numerical optimisation. Simulation studies demonstrate accurate recovery of the pathway-specific degrees-of-freedom parameters together with consistently improved clustering performance relative to the deep Gaussian mixture model under heavy-tailed and contaminated settings. Real-data applications further illustrate the ability of the proposed model to identify heterogeneous latent structures while reducing the influence of atypical observations. The proposed framework retains the hierarchical representation and parsimonious parameter-sharing structure of the deep Gaussian mixture model while providing coherent pathway-wise robustness.

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