Generating data from a hypothetical model Let say I have below model
$y_{i,t} = 1 + X_1 + X_2 + {\gamma}_i + \epsilon_{i,t}, \epsilon_{i,t} \sim N \left(0, 1 \right), {\gamma}_i \sim N \left(0, \gamma \right)$
Here, the panel it is represented by suffix $i$ and there is time dependent measures for each $i$ which is represented by $t$
Another constraint is that, within each panel all observations have correlation coefficient as 0.65.
Based on this model, I need to generate 100 datapoints for further analysis.
Can you please help on pointer how I can I generate data based on above data generation process using R?

This answer assumes the standard deviation of the Normal distribution you're generating $\gamma_i$ from is NOT $\gamma$.
You could generate data by defining a few function in R similar to this:
epsilonGenerator <- function() {
    eps_i = rnorm(1, mean=0, sd=1) 
    return eps_i
}

gammaGenerator <- function(stdev) {
    gamma_i = rnorm(1, mean=0, sd=stdev)
    return gamma_i
}

dataGenerator <- function(X1,X2) {
    y_i = 1 + X1 + X2 + gammaGenerator(stdev) + epsilonGenerator()
    return y_i
}

Then simply call dataGenerator for each each pair of X1 and X2
