Deep learning models are powerful, but often operate as inscrutable "black boxes." What if we could peek inside and understand their decision-making process? DLBacktrace, a new model-agnostic explainability technique, does just that. It traces the relevance of each component from output back to input, illuminating the 'why' behind AI's predictions. Unlike methods like LIME and SHAP, DLBacktrace doesn't rely on external models or baselines, making its interpretations deterministic and consistent across diverse architectures like CNNs, LLMs, and even simple MLPs. Imagine diagnosing why an LLM struggles with a specific math problem or understanding which image features a CNN prioritizes for object recognition. DLBacktrace offers this granular level of insight. Benchmarking tests on various datasets (Lending Club, CIFAR-10, SST-2) show DLBacktrace offers superior robustness and accuracy in its explanations compared to existing techniques. It opens up new possibilities for analyzing network behavior, identifying biases, ensuring fairness, and ultimately, building more trustworthy and transparent AI. While DLBacktrace demonstrates compelling results, challenges remain, such as optimizing inference time for real-time applications and extending its capabilities to increasingly complex model architectures. However, this innovative approach marks a significant step toward demystifying AI and empowering us to harness its full potential responsibly.