One of the neurological conditions that affects the emotional and psychological condition of individuals is known as Epilepsy. Managing this disorder is very challenging as the focal seizures begin in specific brain regions and evolve into generalized forms. The fundamental method for seizure identification is the analysis of the ElectroEncephaloGram (EEG), yet its manual interpretation is prone to error. In addition, the process of automated seizure detection using EEG data suffers from variations between subjects and datasets distribution as they might result in inconsistencies. Moreover, the unequal proportion of seizure to non-seizure samples increases the detection challenge. Hence, developing classification approaches capable of distinguishing and predicting the focal and generalized seizures is important for effective treatment planning. Therefore, an efficient deep learning-based focal and generalized epilepsy classification is designed in this research by considering the multimodal data. Initially, essential signals used for the validation are sourced from publically available EEG datasets and they are converted into Short-Time Fourier Transform (STFT) images, which are considered as the feature set 1. Next, the Sensor data used for the validation are gathered from Kaggle (https://www.kaggle.com/datasets/datasetengineer/epilepsy-dataset) and it is considered as feature set 2. Next, the acquired two set of features are offered to the Multilevel Spatio Temporal Attention Fusion Network (MSTAFN) to execute the feature fusion process. Once the feature fusion procedure is completed, and then the fused features are given as the input to the focal and generalized epilepsy classification phase. In this phase, Adaptive Variational autoencoders with Dense Bidirectional Gated Recurrent Unit (AVDBiGRU) are employed to perform the classification process. Moreover, the focal and generalized epilepsy classification process is improved by optimizing the hyper parameters of AVDBiGRU through Fitness-based Football Optimization Algorithm (FFbOA). Finally, the focal and generalized epilepsy classified outcome is obtained from AVDBiGRU. Further, various experiments are carried out in the developed focal and generalized epilepsy classification model over the widely adopted deep learning architectures like LSTM, DCNN, InceptionV3 and BiGRU to verify their efficiency over different classes. The proposed model is evaluated on an EEG dataset containing the high-frequency oscillation (HFO) annotations from 30 pediatric patients with epilepsy and model performance is assessed by using the standard evaluation metrics that includes accuracy, sensitivity, specificity, and F1-score. The proposed model achieved 95.54% accuracy, 96.66 % specificity, 93.32% F1-score and 88.53 AUC.