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Let $p_1(cdot), p_2(cdot)$ be two discrete distributions on $mathbb{Z}.$ Total variation distance is defined as $d_{TV}(p_1,p_2)= frac{1}{2} displaystyle sum_{k in mathbb{Z}}|p_1(k)-p_2(k)|$ and Shannon entropy is defined the usual way, i.e
$$
H(p_1)=sum_k p_1(k) log(frac{1}{p_1(k)})
$$
Binary entropy function $h(cdot)$ is defined by $h(x)=x log(1/x)+(1-x)log(1/1-x), forall x in (0,1)$

I am trying to prove that $H(frac{p_1+p_2}{2})-frac{1}{2}H(p_1)-frac{1}{2}H(p_2) leq h (d_{TV}(p_1,p_2)/2)$. Can anyone guide me in this direction ?

Below the second part is rather inelegant, and I think this can possibly be improved. Suggestions are welcome.

Note that the LHS is the Jensen-Shannon divergence ($mathrm{JSD}$) between $P_1$ and $P_2$, and that $mathrm{JSD}$ is a $f$-divergence. For $f$-divergences generated by $f, g$ the joint ranges of $D_f,D_g$ are defined as
begin{align}
mathbb{R}^2 supset mathcal{R} :=& { (D_f(P|Q), D_g(P|Q)): P, Q textrm{ are distributions on some measurable space} } \
mathcal{R}_k :=& { (D_f(P|Q), D_g(P|Q)): P, Q textrm{ are distributions on } ([1:k], 2^{[1:k]} ) }end{align}

A remarkable theorem of Harremoees & Vajda (see also these notes by Wu) states that for any pair of $f$-divergences, $$mathcal{R} = mathrm{co}(mathcal{R}_2),$$ where $mathrm{co}$ is the convex hull operator.

Now, we want to show the relation $mathrm{JSD} le h(d_{TV}).$ Since both $mathrm{JSD}$ and $d_{TV}$ are $f$-divergences, and since the set $mathcal{S} := { y - h(x) le 0}$ is convex in $mathbb{R}^2$, it suffices to show this inequality for distributions on $2$-symbols, since by the convexity we have $mathcal{R}_2 subset mathcal{S} implies mathrm{co}(mathcal{R}_2) subset mathcal{S},$ as the convex hull of a set is the intersection of all convex sets containing it. The remainder of this answer will thus concentrate on showing $mathcal{R}_2 subset mathcal{S}$.

Let $p := pi + delta, q:= pi - delta,$ where $delta in [0,1/2]$ and $pi in [delta, 1- delta].$ We will show that $$ mathrm{JSD}(mathrm{Bern}(p)|mathrm{Bern}(q) ) le hleft(frac{1}{2}d_{TV}(mathrm{Bern}(p)|mathrm{Bern}(q) )right) = h(delta), tag{1}$$ which suffices to show the relation on $2$-letter distributions. Note that above $pge q$ always, but this doesn't matter since both $mathrm{JSD}$ and $d_{TV}$ are symmetric in their arguments.

For conciseness I'll set represent the $mathrm{JSD}$ above by $J$. All '$log$'s in the following are natural, and we will make use of the simple identities for $p in (0,1)$ $$ frac{mathrm{d}}{mathrm{d}p} h(p) = log frac{1-p}{p} \ frac{mathrm{d}^2}{mathrm{d}p^2} h(p) = -frac{1}{p} - frac{1}{1-p}. $$

By the expansion in the question, $$J(pi, delta) = h( pi) - frac{1}{2} h(pi + delta) - frac{1}{2} h(pi - delta).$$

It is trivial to see that the relation $(1)$ holds if $delta = 0$. Let us thus assume that $delta > 0.$ For $pi in (delta, 1-delta),$ we have

begin{align} frac{partial}{partial pi} J &= log frac{1-pi}{pi} - frac{1}{2} left( log frac{1 - pi - delta}{pi + delta} + log frac{1 - pi +delta}{pi - delta}right) end{align} and begin{align} frac{partial^2}{partial pi^2} J &= frac{1}{2} left( frac{1}{pi + delta} + frac{1}{pi - delta} + frac{1}{1 - pi - delta} + frac{1}{1 - pi + delta} right) - frac{1}{pi} - frac{1}{1-pi} \
&= frac{pi}{pi^2 - delta^2} - frac{1}{pi} + frac{1 - pi}{( 1-pi)^2 - delta^2} - frac{1}{1-pi} \
&= frac{delta^2}{pi(pi^2 - delta^2)} + frac{delta^2}{(1-pi)( (1-pi)^2 - delta^2)} > 0,
end{align}

where the final inequality uses $delta > 0,$ and that $ pi in (delta, 1-delta).$

As a consequence, for every fixed $delta >0,$ $J$ is strictly convex on $(delta, 1-delta).$ Since the maxima of a convex function on an interval must lie on the end points, we have $$ J(pi ,delta) le max( J(delta, delta), J(1- delta, delta) ).$$

But $$J(delta, delta) = h(delta) - frac{1}{2} (h(2delta) + h(0) ) = h(delta) - frac{1}{2} h(2delta),$$ and similarly $$J(1-delta, delta) = h(delta) - frac{1}{2} h(1-2delta) = h(delta) - frac{1}{2} h(2delta),$$ by the symmetry of $h$. We immediately get that for every $delta in [0,1/2], pi in [delta, 1-delta],$ $$J(pi, delta) le h(delta) - frac{1}{2} h(2delta) le h(delta),$$ finishing the argument.

Note that the last line indicates something stronger for $2$-symbol distributions: $J(pi, delta) le h(delta) - h(2delta)/2$. Unfortunately the RHS is a convex function of $delta$, so this doesn't directly extend to all alphabets. It'd be interesting if a bound that has such an advantage can be shown in general.