Jellyfish have long been reported to be important creatures in marine systems, but the complexity of categorizing the animals, identifying the species, and analyzing their color patterns are some of the biggest concerns when using conventional techniques. This work outlines a new multi-task approach to mitigating these issues in machine learning. With an extensive dataset of jellyfish images at hand, our model approaches the tasks of classification, species identification, and color pattern recognition simultaneously, thus increasing the productivity of research on this phylum. To train our model, we used complicated techniques in feature extraction coupled with a multi-layer neural network, a scenario that outperformed other single-task models. The classification component of the model proposed herein was quite accurate and depicted various species of jellyfish among others well. Another aspect was the combined identification of the species by color pattern analysis, which significantly helped in the identification and identified unique patterns. We can therefore conclude that the general multi-task approach improves classification and identification while providing more information about the color pattern variations across different species of the same image. The findings of this study also signify the possibility of adopting multi-task learning in marine biology while opening the direction for other scholars to apply this approach to other marine organisms. In this work, we offer an automatic technique to recognize various types of jellyfish which simplifies such species recognition and analysis; therefore, it helps in the active and efficient monitoring of further marine species and their interaction with the environment, leading to a better understanding of marine biodiversity.