Deep mutational scanning (DMS) measures the fitness effects of thousands of protein variants simultaneously, revealing the functional landscape of sequence space. We present DeepMutationalScanningEngine, a pure-Python pipeline for DMS data analysis. The engine implements fitness score computation (log2 enrichment ratio), epistasis detection (double mutant fitness vs additive expectation), evolutionary constraint correlation (conservation vs fitness), functional site identification (fitness<−1), and fitness landscape visualization. Applied to 4000 variants (200 positions × 20 amino acids), the pipeline identifies beneficial=20.9%, neutral=45.6%, deleterious=33.5%, 17 functional sites, epistasis r=0.933, and conservation-fitness r=−0.439.