Classical survival models usually struggle to simultaneously capture the dual dynamics of hazard namely: short term and long term risk within a unified framework. Although the Cox proportion hazard model provide flexibility through its semi-parametric structure but it fails to distinctively distinguish between transient and persistent risk mechanism. In contrast, fully parametric models such as Weibull distribution give structural interpretation but lack the flexibility of covariate-driven hazard evolution. This study develops a convoluted proportion hazard model that integrates this complementary strength into a single framework. The developed model combines a Cox proportional hazard component with a parametric Weibull baseline structure and with constrained parameters p and q where p, q  0,1 thereby enabling explicit decomposition of short and long term hazard contributions. Estimation were done using maximum likelihood estimation, procedures parameter are derived explicitly including closed-form expressions for the log-likelihood function and its corresponding score equations. Simulation was conducted to estimate the parameters and model performance is evaluated using standard information criteria and likelihood based comparison measures. Finally, the empirical analysis demonstrates that the developed model outperform the traditional models and fuilt parametric alternatives. The model offers a clear interpretation of risk dynamics. The new model contributes to survival methodology by introducing a structure yet flexible approach capable of disentangling short term and long term hazard effect within a unified proportional hazard setting.