With ever-increasing demand on Internet of Things (IoT) and cloud services, robust security measures are needed to defend the evolving circumstances of cyberthreats, safeguarding the flexibility of interconnected systems and the security of sensitive data. Effectual Android malware detection (AMD) method develops necessarily to increase the security of mobile applications and support their long-term sustainability. Recent research integrates machine learning (ML) approaches and feature engineering to recognize malicious code and protect users from possible risks. By enhancing the security of Android applications, developers and users are more confident in the sustainability and reliability of the mobile app platform. This manuscript offers the design of a Spotted Hyena Optimizer with a Hybrid Deep Learning Enabled Android Malware Detection (SHOHDL-AMD) technique for sustainable applications. The purpose of the SHOHDL-AMD approach is to exploit feature selection (FS) with an optimal DL model for the AMD process. To accomplish this, the SHOHDL-AMD technique employs the SHO technique for feature subset selection (SHO-FSS) technique for choosing an optimal set of features. Besides, convolutional neural network with sparse autoencoder (CNN-SAE) method for AMD process. Moreover, the reptile search algorithm (RSA) was employed for tuning the hyperparameter values of the CNN-SAE methodology. The simulation rate of the SHOHDL-AMD methodology can be tested on the CICAndMal2017 dataset. The comparative evaluation reported the greater solution of the SHOHDL-AMD methodology with other approaches to the AMD process.