Spoken Language Recognition (SLR) is a fundamental challenge in multilingual speech processing, with applications in voice recognition, speech translation, document retrieval, and security. Traditional techniques frequently encounter difficulties in attaining robustness amid diverse speaker variances, encompassing factors such as gender, age, and speaking style. To resolve these challenges, this study introduces an innovative framework known as Golden Jackal-Spoken Language Recognition (GJ-SLR), integrating deep learning, neuro-fuzzy categorization, and metaheuristic optimization. The proposed method initially transforms speech data into spectrogram representations to capture both temporal and spectral characteristics. The SE-DenseNet model is used to extract features, ensuring the clear differentiation of language-specific attributes. The Golden Jackal Optimization (GJO) approach is used to identify the optimal hyperparameters for the SE-DenseNet architecture, hence enhancing the model’s performance. An Advanced Neuro-Fuzzy Learning System (ANFLS) enhances classification, facilitating improved decision-making in challenging situations. The GJ-SLR model demonstrates exceptional performance on a multilingual dataset,reaching 99.26% accuracy, 98.99% precision, and 98.77% recall. Comparative study indicates that it surpasses advanced models such as CNN, SVM, VGG16, and ResNet50.