Quality of service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only consume a few of the broad range of existing services. Thereby, performing a highly accurate service recommendation becomes a challenging task. To tackle the aforementioned challenges, a hybrid deep learning technique is proposed to predict future QoS parameters. Initially, the data is collected from the WSDream data repository, however, the collaborating filtering (CF) technique is used to pre-process the collected data. Accordingly, the filtering technique resolves missing or undermined values and cleans the node. Additionally, this pre-processing technique reduces the error and promotes prediction efficiency. Subsequently, data clustering is performed by introducing a modified k-medoids algorithm, which groups the user services into different clusters. Accordingly, a future QoS is predicted based on the proposed hybrid deep learning method like convolutional neural network-long short-term memory. The proposed method is implemented using Python software and it evaluates response time, throughput, root mean square error, and mean absolute error (MAE). The proposed method is compared with the existing collaborative filtering-k medoids, support vector regression, self-exciting threshold auto-regressive, genetic guided clustering algorithm algorithms, and novel deep learning-based hybrid approach. The result of the proposed method presents higher accuracy and low consumption time. Therefore, the performance of the proposed method is 6% higher than the other existing methods. Accordingly, the MAE, mean absolute percentage error, mean absolute scaled error, and root mean squared error of the proposed method produces 7%, 7.5%, 2%, and 1.5% higher performance than the other existing methods.Subsequently, the precision and recall of the proposed method are approximately 3.5% and 3% high than the other state of art methods. The experimental results highlight that the viability of the proposed approach contrasted with the regular methodologies in real-time. Consequently, by introducing this proposed method the QoS prediction performance is improved and it provides higher-quality solutions.