Epilepsy　is　a　chronic　neurological　disorder　characterized　by　recurrent　seizures.　Accurate　diagnosis　and　effective　monitoring　require　the　precise　classification　of　electroencephalogram　(EEG)　signals.　In　this　study,　we　introduce　a　novel　approach　that　combines　Adaptive　Local　Iterative　Filtering　(ALIF)　for　signal　decomposition　with　an　attention-enhanced　cascaded　deep　neural　network　(CDNN)　architecture.　The　ALIF　algorithm　decomposes　EEG　signals　into　intrinsic　mode　functions　(IMFs)　that　capture　inherent　oscillatory　components.　These　IMFs　are　processed　by　the　CDNN,　which　operates　in　two　stages:　a　feature　extraction　module　and　a　classification　module.　In　the　feature　extraction　stage,　a　SEblock　channel　attention　mechanism　dynamically　prioritizes　significant　features　within　the　IMFs.　The　classification　stage　employs　a　hybrid　CNN-LSTM　architecture　that　effectively　captures　both　spatial　and　temporal　dependencies.　To　enhance　interpretability,　the　SHapley　Additive　exPlanations　(SHAP)　framework　is　incorporated　to　provide　insights　into　the　model＇s　decision-making　process,　while　Gradient-weighted　Class　Activation　Mapping　(Grad-CAM)　visualizes　the　most　discriminative　regions　in　the　input　data.　Rigorously　validated　using　10-fold　cross-validation　on　the　Bonn　and　EEG　Epilepsy　databases,　the　proposed　methodology　achieved　an　exceptional　classification　accuracy　of　100%,　with　sensitivity,　specificity,　and　F1-scores　exceeding　99%　across　various　scenarios.　The　integration　of　SHAP　and　Grad-CAM　not　only　elucidates　the　model＇s　decision　processes　but　also　contributes　to　a　more　interpretable　and　reliable　system　for　epileptic　EEG　signal　classification.　This　synergistic　combination　of　advanced　signal　processing,　deep　learning,　and　interpretability　techniques　holds　significant　potential　to　enhance　epilepsy　diagnosis　and　strengthen　trust　in　clinical　decision　support　systems.