This paper introduces the concept of Distributed and Intelligent Integrated Sensing and Communications (DISAC) for 6G wireless networks, which extends the emerging Integrated Sensing and Communications (ISAC) framework. DISAC addresses the limitations of existing ISAC models by introducing two novel functionalities: a distributed architecture and a semantic and goal-oriented framework. The distributed architecture enables large-scale, energy-efficient tracking of connected users and objects through the fusion of heterogeneous sensors. The semantic and goal-oriented framework transitions from classical data fusion to the composition of semantically selected information, optimizing resource utilization and enhancing multi-modal sensing performance across various use cases. The paper details the principles, architecture, and potential applications of DISAC. It highlights the need for DISAC in addressing the challenges of current ISAC, such as limited support for distributed deployments, a narrow focus on key performance indicators (KPIs), and the lack of integration with external sensors. DISAC is built on several interrelated cornerstones, including a distributed architecture, a semantic and goal-oriented framework, and advanced high-resolution processing. These components enable energy-efficient, high-resolution tracking, intelligent resource allocation, and exceptional multi-modal sensing performance. The paper also discusses the necessity of DISAC from the perspectives of use cases and standardization. It outlines over 30 potential use cases enabled by integrated sensing and 5G/6G communication technology, emphasizing the importance of DISAC for high-precision spatial-temporal processing and prediction. Additionally, it reviews the progress of standardization efforts in the field, including initiatives by organizations like ETSI, 3GPP, and ITU-R WP5D, and highlights the role of AI/ML in enhancing sensing and communication capabilities. Finally, the paper explores the technological enablers of DISAC, including a native semantic framework, an optimized and parsimonious physical layer, intelligent resource allocation, and an evolved network architecture. It addresses the challenges in realizing the DISAC vision, such as designing the semantic framework, developing new hardware components, and coordinating standardization efforts.This paper introduces the concept of Distributed and Intelligent Integrated Sensing and Communications (DISAC) for 6G wireless networks, which extends the emerging Integrated Sensing and Communications (ISAC) framework. DISAC addresses the limitations of existing ISAC models by introducing two novel functionalities: a distributed architecture and a semantic and goal-oriented framework. The distributed architecture enables large-scale, energy-efficient tracking of connected users and objects through the fusion of heterogeneous sensors. The semantic and goal-oriented framework transitions from classical data fusion to the composition of semantically selected information, optimizing resource utilization and enhancing multi-modal sensing performance across various use cases. The paper details the principles, architecture, and potential applications of DISAC. It highlights the need for DISAC in addressing the challenges of current ISAC, such as limited support for distributed deployments, a narrow focus on key performance indicators (KPIs), and the lack of integration with external sensors. DISAC is built on several interrelated cornerstones, including a distributed architecture, a semantic and goal-oriented framework, and advanced high-resolution processing. These components enable energy-efficient, high-resolution tracking, intelligent resource allocation, and exceptional multi-modal sensing performance. The paper also discusses the necessity of DISAC from the perspectives of use cases and standardization. It outlines over 30 potential use cases enabled by integrated sensing and 5G/6G communication technology, emphasizing the importance of DISAC for high-precision spatial-temporal processing and prediction. Additionally, it reviews the progress of standardization efforts in the field, including initiatives by organizations like ETSI, 3GPP, and ITU-R WP5D, and highlights the role of AI/ML in enhancing sensing and communication capabilities. Finally, the paper explores the technological enablers of DISAC, including a native semantic framework, an optimized and parsimonious physical layer, intelligent resource allocation, and an evolved network architecture. It addresses the challenges in realizing the DISAC vision, such as designing the semantic framework, developing new hardware components, and coordinating standardization efforts.