A Comprehensive Review on Contemporary Challenges and Emerging Solutions in Deep Learning and Machine Learning: Limitations, Trends, and Future Directions

Summary

This review paper analyzes deep learning and machine learning challenges, including data bias and scarcity, poor interpretability, weak generalization, adversarial vulnerability, hallucinations, and high computational and energy costs. Drawing on literature published from 2022 to 2026, it examines emerging solutions such as Vision Transformers, Mamba models, quantization, pruning, LoRA, federated learning, SHAP, LIME, and Retrieval-Augmented Generation. It concludes by outlining research gaps and future directions. Published in the International Journal of Science and Research on 2026-11-06.