Suggestion for stacked modelling in machine learning
Would that be called as stacked modelling? 


Yes, this is exactly what stacking models means.

I didn't understand what is the role of clustering in your design? 

The standard approach consists in using only the predictions of the $N$ individual learners as feature for the meta-model. The training/testing data split is a bit more complex: the training of the meta-model requires predictions from the individual models, so the data could be split like this:


training data for individual models
training data for the meta-model (on which individual models are applied)
testing data for the full system 


Generally it's safer to choose a simple learning algorithm for the meta-model, because there is a higher risk of overfitting (for instance linear regression or majority voting).



[Detailed answer to comments below]

The standard setting would be like this:


Train $N$ individual models $m_1,...,m_N$ using first-level training set $T_1$
Apply these models in order to obtain $N$ features for the meta-classifier. At this stage it's a bad idea to use instances from $T_1$, because the individual classifiers have been trained on $T_1$. so one needs a second set of instances $T_2$: for every instance $x\in T_2$, let $m_1(x),...,m_N(x)$ be the predictions resulting from applying classifiers $m_1,...,m_N$.
Train the meta-classifier model $M$ using $T_2$ as training set, with $m_1(x),...,m_N(x)$ the features for any $x\in T_2$. 
Testing: as usual one needs a fresh set of instances, say $T_3$. For any instance $x\in T_3$ calculate the prediction by applying $M$ to the $N$ predictions obtained by the individual models, i.e. $M(m_1(x),...,m_N(x))$