The children under the age of 5 years with Sickle Cell Disease (SCD) are at high risk of having developmental delay. This is of a major concern because the delay is due to the frequent episodes of chronic anemia, crises and cerebrovascular complications on cognitive and motor development. Early detection is essential for timely intervention but the current screening methods lack predictive accuracy and accessibility. This study proposes DevSickleNet, a multimodal deep learning model that integrates clinical, caregiver, and milestone time-series data to predict developmental delays in children with SCD. A synthetic dataset of 300 samples was generated. This incorporates clinical features (hemoglobin levels, pain crises history, stroke history), caregivers factors (educational factors, socioeconomic status), and developmental milestones progression over a span of 12 months. The DevSickleNet achieved an accuracy of 85.4,a precision of 82.9, a recall of 79.5, a F1-score of 81.1 and a ROC-AUC of 0.87, outperforming traditional models like Logistic Regression, Random Forest and XGBoost. The feature importance analysis identified pain crises frequency, levels of hemoglobin and the educational status of the caregivers as the key predictors of developmental delays. These results highlight the potential of AI-driven multimodal learning for the early developmental delay screening in SCD patients. Even though the findings are promising, there is still a need to validate the model using a clinical dataset obtained from tertiary healthcare facilities in order to confirm its clinical applicability. DevSickleNet therefore provides for AI-powered early detection of developmental delay in SCD children and intervention strategies aiming to improve their developmental outcome.