Early prediction of students' placement outcomes is critical for aligning curricula with industry demands, optimizing academic planning, and providing focused career support. It also enhances institutional reputation, strengthens employer partnerships, and supports data-driven decision-making. However, predictive modeling in this context is challenged by data heterogeneity, evolving market factors, subjective evaluations, and bias mitigation. This study proposes an AI-driven framework that integrates Gated Recurrent Unit (GRU) networks with Modified Dwarf Mongoose Optimization (MDMO) to address these challenges. GRU effectively captures temporal patterns in academic and behavioral data, while MDMO ensures optimal hyperparameter tuning through advanced search strategies. Model performance was rigorously evaluated using multiple metrics including accuracy, false positive rate (FPR), false negative rate (FNR), sensitivity, specificity, and Matthews Correlation Coefficient (MCC). The proposed GRU-MDMO model achieved an accuracy of 98.5%, sensitivity of 97.78%, specificity of 99.09%, and MCC of 96.97%, outperforming other baseline models such as SVM, ANN, RF, and traditional GRU variants. These results demonstrate the model’s robustness, reliability, and suitability for early placement prediction. This approach empowers institutions to improve placement rates, enhance curriculum design, attract admissions, and ultimately foster better student career outcomes through AI-guided educational intelligence.