Human activity recognition (HAR) has recently gained popularity due to its applications in healthcare, surveillance, human-robot interaction, and various other fields. Deep learning (DL)-based models have been successfully applied to the raw data captured through inertial measurement unit (IMU) sensors to recognize multiple human activities. Despite the success of DL-based models in human activity recognition, feature extraction remains challenging due to class imbalance and noisy data. Additionally, selecting optimal hyperparameter values for DL models is essential since they affect model performance. The hyperparameter values of some of the existing DL-based HAR models are chosen randomly or through the trial-and-error method. The random selection of these significant hyperparameters may be suitable for some applications, but sometimes it may worsen the model's performance in others. Hence, to address the above-mentioned issues, this research aims to develop an optimized DL model capable of recognizing various human activities captured through IMU sensors. The proposed DL-based HAR model combines convolutional neural network (CNN) layers and bidirectional long short-term memory (Bi-LSTM) units to simultaneously extract spatial and temporal sequence features from raw sensor data. The Rao-3 metaheuristic optimization algorithm has been adopted to identify the ideal hyperparameter values for the proposed DL model in order to enhance its recognition performance. The proposed DL model's performance is validated on PAMAP2, UCI-HAR, and MHEALTH datasets and achieved 94.91%, 97.16%, and 99.25% accuracies, respectively. The results reveal that the proposed DL model performs better than the existing state-of-the-art (SoTA) models.