Objective This study aimed to determine whether quantitative volumetric measures from MRI and metabolic indices from FDG-PET can independently or jointly predict surgical outcome in patients with drug-resistant temporal lobe epilepsy (TLE), particularly when accounting for the well-established prognostic role of hippocampal sclerosis (HS) co-pathology. Methods We retrospectively reviewed patients who underwent temporal lobe resection for drug-resistant epilepsy, comprising 80 with MRI data, 77 with FDG-PET data, and a subset of 42 with both modalities available (fusion cohort). Quantitative asymmetry indices for temporal lobe subregions were derived using the AAL atlas. To assess whether imaging features contributed prognostic information beyond HS status and clinical covariates, we employed hierarchical logistic regression. Data-driven predictive performance was further evaluated through machine learning models using nested leave-one-out cross-validation with permutation testing. Results Across all cohorts, HS co-pathology consistently emerged as the strongest predictor of favorable outcome (MRI cohort: OR=18.4, P<0.001; PET cohort: OR=42.0, P<0.001). When examined individually, neither MRI-derived nor PET-derived quantitative features added significantly to the predictive model beyond HS (MRI: P=0.085; PET: P=0.386). By contrast, combining both modalities in the fusion cohort yielded a significant incremental contribution over HS and clinical variables (likelihood ratio test, P=0.009), with the AUC rising from 0.778 to 0.963. Of particular interest, amygdala volumetric asymmetry on MRI was identified as an independent predictor not previously reported (OR=83.7, P=0.041). Machine learning approaches yielded only modest discrimination (fusion cohort AUC=0.692, P=0.075) and did not outperform the hypothesis-driven statistical framework. Conclusion Integrating MRI volumetric and FDG-PET metabolic data offers meaningful prognostic value that extends beyond what HS status alone can provide. Amygdala asymmetry on MRI represents a novel independent predictor warranting further validation. Our findings favor comprehensive multimodal presurgical workup over reliance on a single imaging modality and suggest that, for surgical outcome prediction in TLE, hypothesis-driven analytical approaches may hold advantages over purely data-driven machine learning strategies.