The recent developments in the research area of neural network interpretability have shown a strong focus on creating model-agnostic and multi-modal approaches that enhance transparency and accountability in AI systems. A significant trend is the integration of novel techniques such as Squeeze and Excitation (SE) blocks and Layer-Wise Relevance Propagation (LRP) to generate visual attention heatmaps and assess feature importance. These methods aim to provide comprehensive views of neural network decision-making processes, particularly in sensitive applications like biometrics, security, and healthcare. Additionally, there is a growing emphasis on developing frameworks that merge various data modalities, such as audio, face, and body information, to improve the robustness and interpretability of models in challenging conditions. The advancements also highlight the importance of creating summary model explanations and leveraging human-in-the-loop frameworks to enhance trust and transparency in AI systems. Notably, the integration of Large Language Models for interpreting visualizations and optimizing workflows is emerging as a promising direction. Overall, the field is moving towards more scalable, efficient, and interpretable AI systems that can be trusted in real-world applications.