Algorithmic Reconstruction of Extinct Species Phenotype via Multi-faceted Genomic Data Integration and Predictive Ecological Modeling: A Case Study on *Woolly Mammoth (Mammuthus primigenius)*

**Abstract:** The resurrection of extinct species holds immense potential for ecological restoration and scientific advancement. However, current methods relying solely on genomic reconstruction face limitations in accurately predicting complex phenotypic traits. This research presents a novel framework, termed *Pheno-Eco-Predict* (PEP), which integrates multi-faceted genomic data (ancient DNA, epigenetic markers, transcriptomic data) with advanced ecological modeling techniques to predict the phenotype of *Woolly Mammoth (Mammuthus primigenius)* and assess its potential ecological impact upon reintroduction to suitable Pleistocene rewilding ecosystems. PEP significantly surpasses existing methods by incorporating ancestral environmental context and leveraging Bayesian inference to quantify uncertainty in phenotypic reconstructions, offering a path toward ecologically responsible de-extinction programs.