  We present a comprehensive test of the cosmic distance duality relation (DDR)
using a combination of strong gravitational lensing (SGL) time delay
measurements and Type Ia supernovae (SNe Ia) data. We investigate three
different parameterizations of potential DDR violations. To bridge the gap
between SGL and SNe Ia datasets, we implement an artificial neural network
(ANN) approach to reconstruct the distance modulus of SNe Ia. Our analysis
uniquely considers both scenarios where the absolute magnitude of SNe Ia
($M_B$) is treated as a free parameter and where it is fixed to a
Cepheid-calibrated value. Using a sample of six SGL systems and the Pantheon+
SNe Ia dataset, we find no statistically significant evidence for DDR
violations across all parameterizations. The consistency of our findings across
different parameterizations not only reinforces confidence in the standard DDR
but also demonstrates the robustness of our analytical approach.
