Deep learning has been able to achieve impressive results in recent years. But the availability of suitable amount of domain-specific data remains a challenge especially in image related tasks where dataset can vary in size from a few hundred images to millions of images. A sufficiently deep neural network with millions of parameters needs huge amount of data for training. On the other hand, training deep neural networks on a large dataset is computationally expensive. In this context, this thesis explores an ensemble learning based approach for image recognition. Multiple pre-trained Convolution Neural Networks (CNNs) are fine-tuned as base learners, and they are combined using a meta learner to improve the overall performance. This approach provides reasonable accuracy with comparatively less computational cost. As part of this study, a novel regression model is developed with 3 CNNs as base learners and a feed-forward neural network as meta-learner to predict the value of fine particulate matter (PM2.5) from image using a small image dataset. The experimental results demonstrate that the proposed method provides a more accurate PM 2.5 prediction compared to the individual CNNs and therefore it can be used for image-based PM2.5 estimation. A relatively similar approach is applied for a classification task with six CNNs as base learners using a large image dataset. In this case also, the ensemble-based approach outperforms the individual CNNs in terms of classification accuracy.