The role of artificial intelligence-based decision-making processes in the recent advancements of data-driven technology is inevitable, but the key challenge is embedded here in the opacity of their decision-making mechanisms, presenting difficulties in explaining these processes to the end users. The well-known RISE method (randomized input sampling for explanations) and its variants are widely used now-a-days for explainability with image data through perturbative approach. However, due to significantly large number of forward passes as required for increasing number of mask generation, RISE is heavy in computation. The issue is potentially addressed in this paper by intelligently sampling fewer number of masks through a guided scheme, instead of using large number of randomly generated masks. Our proposed approach of guided input sampling-based explanations (GuISE), introduces an innovative method for generating an importance map, illustrating the saliency of each pixel in the model’s predictions. Unlike white-box explanation schemes that depend on gradients or internal network states for pixel importance estimation, GuISE functions as a black-box approach and outperforms particularly in its masking technique. To validate our approach, we compare it against the state-of-the-art importance extraction methods using both automatic deletion and insertion metrics. Extensive experiments on benchmark image datasets demonstrates comparable or superior performance of our proposed GuISE, even surpassing the white-box approaches. This highlights the effectiveness of GuISE in achieving explainability of deep neural networks for image-based applications.