We fix any dimension \( d \ge 1 \) and consider the space of continuous functions \( \Omega = C([0,\infty), R^d) \) endowed with the topology of uniform convergence of compact subsets. \( \Omega \) is tacitly equipped with the Wiener measure \( P_x \) corresponding to an \( R^d \) valued Brownian motion \( \omega \) starting in \( x\in R^d \). The Gaussian field \( \{H_T \omega)\}_{\omega\in\Omega} \) at level \( T>0 \) then is given by $$ H_T (\omega) = \int_0^T \int_{R^d} \kappa (\omega_s-y) B(s,y)dyds, $$ where B is space-time white noise and \( \kappa \) is a non-negative mollifier. The renormalized GMC measure corresponding to the field \( \{ H_T(\omega)\}_{\omega\in\Omega}\) is given by $$ \widehat{\mathscr M}_{\beta,T}(\mathrm d \omega)=\frac 1 {\mathscr Z_{\beta,T}} \exp\bigg\{\beta \mathscr H_T(\omega) - \frac{\beta^2T}{2}(\kappa\star\kappa)(0)\bigg\} \mathbb P_0(\mathrm d \omega) $$ with $\mathscr Z_{\beta,T}$ denoting the total mass. The total mass or partition function is closely related to the solution \(u_{\epsilon,t}(x) \) of the (smoothed) multiplicative noise stochastic heat equation $$ d u_{\epsilon,t}=\frac{1}{2}\Delta u_{\epsilon,t} d t+\beta\epsilon^{\frac{d-2}{2}}u_{\epsilon,t} d B_{\epsilon,t},\qquad u_{\epsilon,0}=1. $$ The parameter $\beta$, known as the inverse temperature, captures the strength of the noise. In a recent work by Mukherjee, Shamov and Zeitouni, it was shown that, for fixed $t>0$ and for $\beta$ small enough, $u_{\epsilon,t}(0)$ converges as $\epsilon\rightarrow 0$ in distribution to a strictly positive random variable, while for $\beta$ large, it converges in probability to zero. For $\beta$ small we have proved a quenched CLT for the endpoint of $\omega$ under $\widehat{\mathscr M}_{\beta,T}$ and for $\beta$ large enough, we proved that the endpoint is localized in random regions of the space. We also considered $\ell^\infty$ balls around the path $\omega$. For these balls there is no localization under the GMC-measure $\widehat{\mathscr M}_{\beta,T}$ for any value of $\beta$.