Traditional numerical methods of classification of both univariate and multivariate time series have shown to be challenging due to their problematic features, like including the inability to preserve temporal correlation, lack of pre-trained models, difficulty in training Timeseries models and finally Timeseries tend to act incorrectly when presented with multiple input. Decades have shown immense importance in applying deep learning methods to image and video data to solve real-world issues. The authors are motivated by recent accomplishments of supervised learning approaches in computer vision, so they have explored various time series image encoding techniques to enable machines to visually identify, classify, and learn structures and patterns by leveraging state of the art Deep learning and computer vision. The Authors demonstrated rapidly spot groupings of series with specific features. Later, these series were classified and subjected to comparison analysis using multiple cutting-edge deep neural networks to their maximum performance potential. Finally, the comparison results were demonstrated.