Fetal neurodevelopment is a complex process of neural growth during pregnancy, where early detection of abnormalities is vital, and deep learning offers promising techniques for this purpose. The objective of this systematic review is to investigate deep learning applications in fetal neurodevelopment, aiming to synthesize cutting-edge research, examine methodologies, identify research gaps, and propose a federated learning framework. Following PRISMA 2020 guidelines, 55 peer-reviewed articles were selected from an initial 900 records across major databases and additional sources where each article was examined through six specific data extraction criteria. Peer-reviewed articles from 2005 to 2025, specifically those exploring automated deep learning for fetal neurodevelopment using clinical images were included, while non-deep learning analyses were excluded. Risk of bias was qualitatively assessed based on design, data diversity, validation, and reporting. Key scopes of the studies included brain segmentation and regionalization (50.91%), structural measurement (12.73%), image reconstruction, enhancement and synthesis (21.82%) and predictive modeling and clinical classification (14.55%) which also distinguishes between tasks involving pixel-level analysis and image-level predictions. The 55 included studies used diverse datasets (753 to 433,000 images) as well as synthetic image data in some recent works covering wide-ranging gestational ages, mainly using MRI and ultrasound images. The systematic analysis explicitly categorizes each study by task type, applied methodology (U-Net variants, transformer-based models, CNNs, implicit neural representations), and corresponding evaluation metrics—segmentation (DSC, IoU, HD95), classification (Accuracy, Precision, AUC), regression (MAE, RMSE, R ² ), and reconstruction (PSNR, SSIM), facilitating standardized performance comparisons and establishing clear benchmarks for future research in automated fetal brain imaging. Significant gaps that were identified include inadequate data diversity, privacy measures, limited clinical interpretability and validity of AI models, and insufficient integration of multimodal data. To address these challenges, a unified framework is proposed that integrates multimodal data fusion, explainable artificial intelligence (XAI) paradigms, and federated learning architectures complemented by synthetic data generation techniques to ensure robust privacy preservation in real-world application. This work was not specifically funded, and the review was not registered.