In the recent era of smart city ecosystems and the Internet of Things (IoT), innovative, intelligent parking systems must make cities more sustainable. Every year, the increasing number of city vehicles requires more time to search for parking slots. In large cities, 10% of the traffic congestion occurs because of cruising; drivers spend almost 20 minutes searching for free space to park their vehicles. The passing time of waiting for parking in the traffic leads the issues such as energy, pollution, and stress. There needs to be more than the developed solutions. Therefore, the necessary to create a parking slot availability detection system that informs the drivers in advance about the free parking slot based on location. This paper introduces an enhanced ensemble Deep Learning (DL) model designed to forecast parking slot availability through the integration of IoT, cloud technology, and sensor networks. The devised model, known as Ensemble CNN-Boosted Graph LSTM (ECNN-BGLSTM), is optimized using a Genetic Algorithm (GA) framework. The model's performance is rigorously evaluated using a dataset from Europe, and various metrics, including Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE), are employed for assessment. The experimental findings demonstrate the superior performance of the proposed model compared to existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]