We invoke the following lemma (which is the special case of the fact that the operator norm of a matrix is equal to its largest singular value):
Lemma. Let $A$ be a $d \times d$ symmetric real positive semidefinite matrix. Then its operator norm $\|A\|$ is equal to the largest eigenvalue of $A$.
Proof. Let $\lambda_1 \geq \lambda_2 \geq \ldots \geq \lambda_d \geq 0$ denote the eigenvalues of $A$, arranged in decreasing order. By the spectral theorem, there exists an orthonormal basis $u_1, \ldots, u_d$ of $\mathbb{R}^d$ such that $Au_i = \lambda_i u_i$ for each $i = 1, \ldots, d$. Then for any vector $v \in \mathbb{R}^d$,
\begin{align*}
\| A v \|
&= \left\| A \sum_{i=1}^{d} \langle u_i, v \rangle u_i \right\|
= \left\| \sum_{i=1}^{d} \lambda_i \langle u_i, v \rangle u_i \right\| \\
&= \left( \sum_{i=1}^{d} \lambda_i^2 \langle u_i, v \rangle^2 \right)^{1/2}
\leq \left( \lambda_1^2 \sum_{i=1}^{d} \langle u_i, v \rangle^2 \right)^{1/2}
= \lambda_1 \|v\|,
\end{align*}
proving the desired claim. $\square$
Next, note that
$$ \frac{\mathrm{d}}{\mathrm{d}t} e^t \operatorname{Tr}(M(t))
= e^t [\operatorname{Tr}(M(t)) + \operatorname{Tr}(M'(t))]
\leq 0. $$
So, $ t \mapsto e^t \operatorname{Tr}(M(t))$ is non-increasing and we get $e^t \operatorname{Tr}(M(t)) \leq \operatorname{Tr}(M(0))$, or equivalently,
$$ \operatorname{Tr}(M(t)) \leq e^{-t} \operatorname{Tr}(M(0)). \tag{1} $$
Finally, invoke the lemma and positive semidefiniteness of $M(t)$ to get
$$ \| M(t) \| \leq \operatorname{Tr}(M(t)). \tag{2} $$
Combining $\text{(1)}$ and $\text{(2)}$, we conclude the desired claim.