A novel dynamic just-in-time (JIT) learning framework is proposed in this paper for the data driven modeling of batch process. In the proposed JIT framework, we employ a searching strategy based on "profile similarity" which takes into account the dynamicity of batch process instead of "sample similarity" measures as reported in previous literature. This is achieved by a "modified edit distance time warping" framework that is proposed in this work. In addition, a new weighting strategy that assigns space, time, and batch weights to data points is introduced to capture the complete dynamics of the batch process. Furthermore, the proposed method can detect and accommodate outliers in the historical data sets. To test the efficacy, we have validated the proposed approach with various simulation studies, namely, (i) a numerical example, (ii) the batch polymerization process, and (iii) the batch transesterification process.