This article examines the use of Explainable Artificial Intelligence (XAI) in conjunction with Deep Learning models to detect Android malware. Grad-CAM, LIME, SHAP, IG, and LRP are XAI techniques that improve model interpretability and provide deeper insights into decision-making processes. Deep Learning models, specifically CNNs and Graph Convolutional Networks (GCNs), outperform traditional methods in identifying malware families from datasets like as VirusShare, Android Malware Genome Project, Drebin, CICMalDroid2020, and AMD. Actual malware samples improve the models’ resilience and effectiveness in real-world circumstances. However, issues like as vulnerability to adversarial attacks, scalability, and generalization across different malware types remain. Researchers and practitioners must work together to improve XAI techniques and strengthen cybersecurity defenses. The combination of XAI and deep learning offers exciting prospects to improve Android malware detection while also providing transparent and dependable solutions for a safer digital world.