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The detection and rapid characterization of earthquake parameters such as magnitude are important in real‐time seismological applications such as Earthquake Monitoring and Earthquake Early Warning (EEW). Traditional methods, aside from requiring extensive human involvement can be sensitive to signal‐to‐noise ratio leading to false/missed alarms depending on the threshold. We here propose a multitasking deep learning model—the Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME) that: (a) detects the earthquake signal from background seismic noise, (b) determines the first P wave arrival time, and (c) estimates the magnitude using the raw three‐component waveforms from a single station as model input. Considering, that speed is essential in EEW, we use up to 2 s of P wave information which, to the best of our knowledge, is a significantly smaller data window compared to the previous studies. To examine the robustness of CREIME, we test it on two independent data sets and find that it achieves an average accuracy of 98% for event versus noise discrimination and can estimate first P‐arrival time and local magnitude with average root mean squared errors of 0.13 s and 0.65 units, respectively. We compare CREIME with traditional methods such as short‐term‐average/long‐term‐average (STA/LTA) and show that CREIME has superior performance, for example, the accuracy for signal and noise discrimination is higher by 4.5% and 11.5%, respectively, for the two data sets. We also compare the architecture of CREIME with the architectures of other baseline models, trained on the same data, and show that CREIME outperforms the baseline models. Plain Language Summary: The detection of earthquakes and rapid determination of parameters such as magnitude is crucial in Earthquake Monitoring and Earthquake Early Warning (EEW). Existing methods used to make such estimations are empirical and require expert analysts to define involved parameters, which is quite challenging. They are also sensitive to noise, which could lead to erroneous results. In this paper, we propose the Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME) which is capable to detect an earthquake within 2 s of the first P wave arrival and provides a first estimate for its magnitude. We test the model on two independent data sets to demonstrate its generalizability. CREIME successfully discriminates between seismic events and noise with an average accuracy of 98% and can estimate first P‐arrival time and local magnitude with average root mean squared errors of 0.13 s and 0.65 units, respectively. We also show that CREIME can perform better than traditional methods like STA/LTA and previously published deep learning architectures in the context of rapid characterization. Key Points: We use a novel sequence‐to‐sequence mapping to train a deep learning model to detect an earthquake, pick the P‐wave arrival and estimate its magnitudeThe proposed model can perform reasonably well with 5 s windows containing only up to 2 s of P wave dataWe show that our model can outperform traditional methods like short‐term‐average/long‐term‐average (STA/LTA) and the existing deep learning models [ABSTRACT FROM AUTHOR]