Code clones are already proven as harmful for maintenance and evaluation of software systems such as development and modification of source code, propagation of potential bugs throughout software system and so on. Now a days, a single software system is developing in various programming languages for greater adaptability. In these systems a single functionality is replicated among their varieties; often implemented in different programming languages (cross language clones(CLC)). Consequences of these clones are more severe as they are hard to detect and hard to track their modifications. However, while there are a great many studies for finding clones in the same programming language, there is a marked lack of studies in detecting CLCs. To fill this gap, in this paper, we are going to propose CLCDSA, a CLC detector which can detect CLCs despite any prerequisites or intermediate states. This model analyses different syntactical features of source codes across different programming languages to detect CLC. To support large scale clone data, this model comprises an action filter based on cross language API calls similarity to discard non-potential clones before proceed to the main model. The design methodologies of CLCDSA is two-folded: a. it can detect CLC On the Fly by calculating features’ value similarity; b. it poses a deep neural network based feature vector learning model to learn the features and detect CLC. An early evaluation of this model observed an average precision, recall and F-measure score of 0.55, 0.86 and 0.64 respectively for the On the Fly phase and 0.61, 0.93 and 0.71 for the neural net phase which indicate that CLCDSA has outperformed all the available models in detecting cross language clones.