diff --git a/dev-python/dtreeviz/Manifest b/dev-python/dtreeviz/Manifest
new file mode 100644
index 0000000000..00c57fe162
--- /dev/null
+++ b/dev-python/dtreeviz/Manifest
@@ -0,0 +1 @@
+DIST dtreeviz-2.2.1.gh.tar.gz 91575827 BLAKE2B 41889e36b58df4fb81cf65b1c8fe89c5206e737a19503f270ed2b4d64b64e1890119d2017b63419af306cfc3747738483fdfed463f0b22049dc54f8b03dc63b0 SHA512 8c80479164cbc2004b27160cc1be5a0d0422bb5b58603bb9a347e8e9d98735fbc8a9fa0635c6605e8ffa360d9fd669bbe88befe74d598c02eb10b675fd2274d6
diff --git a/dev-python/dtreeviz/dtreeviz-2.2.1.ebuild b/dev-python/dtreeviz/dtreeviz-2.2.1.ebuild
new file mode 100644
index 0000000000..797574875f
--- /dev/null
+++ b/dev-python/dtreeviz/dtreeviz-2.2.1.ebuild
@@ -0,0 +1,32 @@
+# Copyright 2023 Gentoo Authors
+# Distributed under the terms of the GNU General Public License v2
+
+EAPI=8
+
+DISTUTILS_USE_PEP517=setuptools
+PYTHON_COMPAT=( python3_{8..11} )
+inherit distutils-r1
+
+DESCRIPTION="A python library for decision tree visualization and model interpretation"
+HOMEPAGE="
+	https://pypi.org/project/dtreeviz/
+"
+SRC_URI="https://github.com/parrt/dtreeviz/archive/refs/tags/${PV}.tar.gz -> ${P}.gh.tar.gz"
+
+LICENSE="MIT"
+SLOT="0"
+KEYWORDS="~amd64"
+
+RDEPEND="
+	>=dev-python/graphviz-0.9
+	dev-python/pandas
+	dev-python/numpy
+	sci-libs/scikit-learn
+	dev-python/matplotlib
+	dev-python/colour
+	dev-python/pytest
+"
+
+src_install() {
+	distutils-r1_src_install
+}
diff --git a/dev-python/dtreeviz/metadata.xml b/dev-python/dtreeviz/metadata.xml
new file mode 100644
index 0000000000..31a85aae37
--- /dev/null
+++ b/dev-python/dtreeviz/metadata.xml
@@ -0,0 +1,13 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE pkgmetadata SYSTEM 'https://www.gentoo.org/dtd/metadata.dtd'>
+<pkgmetadata>
+	<maintainer type="person">
+		<email>torsi@fi.uba.ar</email>
+		<name>Tomas Fabrizio Orsi</name>
+	</maintainer>
+	<longdescription>A python library for decision tree visualization and model interpretation. Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. The visualizations are inspired by an educational animation by R2D3; A visual introduction to machine learning. Please see How to visualize decision trees for deeper discussion of our decision tree visualization library and the visual design decisions we made.</longdescription>
+	<upstream>
+		<remote-id type="github">parrt/dtreeviz</remote-id>
+		<remote-id type="pypi">dtreeviz</remote-id>
+	</upstream>
+</pkgmetadata>