This paper provides a comprehensive survey of Multi-Task Learning (MTL), focusing on algorithmic modeling, applications, and theoretical analyses. MTL aims to leverage information from multiple related tasks to improve the generalization performance of all tasks. The paper classifies MTL algorithms into five categories: feature learning, low-rank, task clustering, task relation learning, and decomposition approaches. It discusses the combination of MTL with other learning paradigms such as semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning, and graphical models. The paper also reviews online, parallel, and distributed MTL models, as well as dimensionality reduction and feature hashing techniques for handling high-dimensional data. Additionally, it covers real-world applications of MTL and presents theoretical analyses, discussing future directions for the field.This paper provides a comprehensive survey of Multi-Task Learning (MTL), focusing on algorithmic modeling, applications, and theoretical analyses. MTL aims to leverage information from multiple related tasks to improve the generalization performance of all tasks. The paper classifies MTL algorithms into five categories: feature learning, low-rank, task clustering, task relation learning, and decomposition approaches. It discusses the combination of MTL with other learning paradigms such as semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning, and graphical models. The paper also reviews online, parallel, and distributed MTL models, as well as dimensionality reduction and feature hashing techniques for handling high-dimensional data. Additionally, it covers real-world applications of MTL and presents theoretical analyses, discussing future directions for the field.