Explanation techniques provide insight on how deep neural networks and other black-box classifiers make decisions. Deep learning models, particularly “deep neural networks” model have shown great performance in wide range of fields. However, it is often difficult to fully understand their decision-making processes due to their inbuilt complexity. This article presents an approach to refine the explainability of CNN models, leveraging two techniques “Gradient-weighted Class Activation Mapping (Grad-CAM)” and “Layer-wise Relevance Propagation (LRP)”. Grad-CAM is used to identify and emphasize the areas of an input that have a significant effect on the predictions made by the model. This method uses the gradient information to generate class activation maps, providing interpretable insights into the model’s focus areas. On the other hand, LRP is introduced as a complementary technique that assigns relevance scores to individual neurons in the network, facilitating a more comprehensive understanding of feature contributions throughout the model. By combining Grad-CAM and LRP, our dual approach aims to offer a more nuanced and transparent explanation of deep learning model decisions. We demonstrate the applicability of this methodology across diverse datasets and model architectures, showcasing its effectiveness in shedding light on the critical factors influencing predictions. Through empirical evaluations, we highlight the synergistic benefits of integrating Grad-CAM and LRP, emphasizing their complementary roles in providing a holistic and accurate interpretation of deep learning model outputs. This dual approach contributes to advancing the field of explainability in deep learning, addressing the growing need for transparency and interpretability in complex neural network models.