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

Deep Learning and Machine Learning have emerged as transformative technologies driving innovation across healthcare, finance, autonomous systems, and generative intelligence. Despite their remarkable success, the rapid scaling and deployment of these models have exposed a series of critical challenges that limit their reliability, efficiency, and real-world applicability. This comprehensive review paper presents a systematic analysis of contemporary problems and prospective solutions in the field, based on literature published between 2022 and 2026. Major challenges identified include data-related issues such as bias, scarcity, poor quality, and privacy concerns; model-centric limitations including the black-box nature leading to lack of interpretability and explainability, poor generalization to out-of-distribution data, and vulnerability to adversarial attacks and hallucinations in generative models. Furthermore, we highlight the escalating computational costs, high energy consumption, and environmental impact associated with training large-scale models, alongside growing ethical and regulatory concerns. In response, this review examines emerging solutions shaping the next generation of AI. These include efficient architectures such as Vision Transformers, Mamba models, model quantization, pruning, and Parameter-Efficient Fine-Tuning (LoRA), data-centric approaches like synthetic data generation and federated learning, and trust-centric methods including Explainable AI (XAI) using SHAP and LIME, Retrieval-Augmented Generation (RAG) for hallucination mitigation, and robust and trustworthy AI frameworks. The paper concludes by outlining key research gaps and future directions towards developing more trustworthy, efficient, scalable, and sustainable AI systems.