Exposure to half-truths or lies has the potential to undermine democracies, polarize public opinion, and promote violent extremism. Identifying the veracity of fake news is a challenging task in distributed and disparate cyber-socio platforms. To enhance the trustworthiness of news on these platforms, in this article, we put forward a fake news detection model, OptNet-Fake. The proposed model is architecturally a hybrid that uses a meta-heuristic algorithm to select features based on usefulness and trains a deep neural network to detect fake news in social media. The$d$-D feature vectors for the textual data are initially extracted using the term frequency inverse document frequency (TF-IDF) weighting technique. The extracted features are then directed to a modified grasshopper optimization (MGO) algorithm, which selects the most salient features in the text. The selected features are then fed to various convolutional neural networks (CNNs) with different filter sizes to process them and obtain the$n$-gram features from the text. These extracted features are finally concatenated for the detection of fake news. The results are evaluated for four real-world fake news datasets using standard evaluation metrics. A comparison with different meta-heuristic algorithms and recent fake news detection methods is also done. The results distinctly endorse the superior performance of the proposed OptNet-Fake model over contemporary models across various datasets.