This study presents a novel approach for predicting future cellular traction in a time series. The proposed method leverages two distinct look-ahead Long Short-Term Memory (LSTM) models—one for cell boundary and the other for traction data—to achieve rapid and accurate predictions. These LSTM models are trained using real Fourier Transform Traction Cytometry (FTTC) output data, ensuring consistency and reliability in the underlying calculations. To account for variability among cells, each cell is trained separately, mitigating generalized errors. The predictive performance is demonstrated by accurately forecasting tractions for the next 30-time instances, with an error rate below 7%. Moreover, a strategy for real-time traction calculations is proposed, involving the capture of a bead reference image before cell placement in a controlled environment. By doing so, we eliminate the need for cell removal and enable real-time calculation of tractions. Combining these two ideas, our tool speeds up the traction calculations 1.6 times, leveraging from limiting TFM use. As a walk forward, prediction method is implemented by combining prediction values with real data for future prediction, it is indicative of more speedup. The predictive capabilities of this approach offer valuable insights, with potential applications in identifying cancerous cells based on their traction behavior over time.Additionally, we present an advanced cell boundary detection algorithm that autonomously identifies cell boundaries from obscure cell images, reducing human intervention and bias. This algorithm significantly streamlines data collection, enhancing the efficiency and accuracy of our methodology.