With the growing adoption of Advanced Driver Assistance Systems (ADAS) in the Indian automotive landscape, the need for effective Driver Monitoring Systems (DMS) has become increasingly critical. This paper presents the design, development, and validation of a Driver Distraction and Attention Warning System (DDAWS) tailored to Indian driving conditions. The proposed system integrates two key modules: Driver Attention Monitoring and Drowsiness Detection, using a high-resolution driver-facing camera to analyse head pose, facial landmarks, and behavioural cues. The drowsiness module incorporates metrics such as PERCLOS and Eye Aspect Ratio (EAR), evaluated against the Karolinska Sleepiness Scale (KSS). Recognizing the limitations of self-assessed scales like KSS in dynamic driving environments, the study compares algorithmgenerated KSS values with self-reported scores to assess model accuracy. Additionally, the framework aligns with automotive safety standards such as AIS184,EU 2021/1341, EU 2023/2590, and EURO-NCAP. A multi-level redundancy architecture is introduced to improve prediction robustness by fusing outputs from both attention and drowsiness subsystems. The result is a scalable, regulation-compliant, and reliable DDAWS framework, optimized for real-world deployment in Indian vehicles.