Recently, the Ethereum smart contracts have seen a surge in interest from the scientific community and new commercial uses. However, as online trade expands, other fraudulent practices—including phishing, bribery, and money laundering—emerge as significant challenges to trade security. This study is useful for reliably detecting fraudulent transactions; this work developed a deep learning model using a unique metaheuristic optimization strategy. The new optimization method to overcome the challenges, Optimized Genetic Algorithm-Cuckoo Search (GA-CS), is combined with deep learning. In this research, a Genetic Algorithm (GA) is used in the phase of exploration in the Cuckoo Search (CS) technique to address a deficiency in CS. A comprehensive experiment was conducted to appraise the efficiency and performance of the suggested strategies compared with those of various popular techniques, such as k-nearest neighbors (KNN), logistic regression (LR), multi-layer perceptron (MLP), XGBoost, light gradient boosting machine (LGBM), random forest (RF), and support vector classification (SVC), in terms of restricted features and we compared their performance and efficiency metrics to the suggested approach in detecting fraudulent behavior on Ethereum. The suggested technique and SVC models outperform the rest of the models, with the highest accuracy, while deep learning with the proposed optimization strategy outperforms the RF model, with slightly higher performance of 99.71% versus 98.33%.